Patent Publication Number: US-9418674-B2

Title: Method and system for using vehicle sound information to enhance audio prompting

Description:
FIELD OF THE INVENTION 
     The present invention is related to enhancing vehicle spoken dialogue prompting using, for example, a combination of sound related vehicle information, signal processing, and other operations or information. 
     BACKGROUND OF THE INVENTION 
     Many vehicles are equipped with spoken dialog, voice activated, or voice controlled vehicle systems. Spoken dialog systems may perform functions, provide information, and/or provide responses based on verbal commands. A spoken dialog system may process or convert sounds (e.g., speech produced by a vehicle occupant) from a microphone into an audio signal. Speech recognition may be applied to the audio signal, and the identified speech may be processed by a semantic interpreter. Based on the interpretation of the verbal command, a system such as a dialogue control system may perform an action, generate a response, or perform other functions. A response may, for example, be in the form of a visual signal, audio signal, text to speech signal, action taken by a vehicle system, or other notification to vehicle occupants. 
     The clarity and decipherability of voice commands may affect the function of a voice activated vehicle system. A microphone may often, however, receive a signal with speech and non-speech related sounds reducing the clarity of voice commands. Non-speech related sounds may include vehicle related noises (e.g., engine noise, cooling system noise, etc.), non-vehicle related noise (e.g., noises from outside the vehicle), audio system sounds (e.g., music, radio related sounds), and other sounds. The non-speech related sounds may often be louder than, overpower, and/or distort speech commands. As a result, a speech recognition system or method may not function properly if non-speech related sounds distort speech commands. Similarly, the accuracy of system such as a dialogue control system in generating responses to speech commands may be reduced by non-speech related sounds. Non-speech related sounds may, for example, distort or overpower text to speech responses, audio, and other signals output from a spoken dialogue system and/or other systems. Thus, a system or method to enhance speech recognition, dialogue control, and/or speech prompting systems based on sound or acoustic related vehicle information is needed. 
     SUMMARY OF THE INVENTION 
     Sound related vehicle information representing one or more sounds may be received in a processor associated with a vehicle. The sound related vehicle information may or may not include an audio signal. An audio signal output to a passenger may be modified based on the sound related vehicle information. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The subject matter regarded as the invention is particularly pointed out and distinctly claimed in the concluding portion of the specification. The invention, however, both as to organization and method of operation, together with objects, features, and advantages thereof, may best be understood by reference to the following detailed description when read with the accompanying drawings in which: 
         FIG. 1  is a schematic illustration of vehicle with an automatic speech recognition system according to an embodiment of the present invention; 
         FIG. 2  is a schematic illustration of an automatic speech recognition system according to embodiments of the present invention; 
         FIG. 3  is a block diagram of a spoken dialogue system according to embodiments of the present invention; 
         FIG. 4  is a block diagram of an automatic speech recognition system according to embodiments of the present invention; 
         FIG. 5  is a block diagram of a spoken dialogue prompting system according to embodiments of the present invention; 
         FIG. 6  is a block diagram of a spoken dialogue system according to embodiments of the present invention; and 
         FIG. 7  is a flow chart of a method according to embodiments of the present invention. 
     
    
    
     It will be appreciated that for simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity. Further, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements. 
     DETAILED DESCRIPTION OF THE PRESENT INVENTION 
     In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the invention. However, it will be understood by those of ordinary skill in the art that the embodiments of the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to obscure the present invention. 
     Unless specifically stated otherwise, as apparent from the following discussions, throughout the specification discussions utilizing terms such as “processing”, “computing”, “storing”, “determining”, or the like, refer to the action and/or processes of a computer or computing system, or similar electronic computing device, that manipulates and/or transforms data represented as physical, such as electronic, quantities within the computing system&#39;s registers and/or memories into other data similarly represented as physical quantities within the computing system&#39;s memories, registers or other such information storage, transmission or display devices. 
     Embodiments of the present invention may use sound related vehicle information (e.g., information on vehicle systems that relates to sounds in the vehicle, but does not itself include sound signals or recordings or audio signals or recordings), signals or information related to the operation of vehicle systems producing or causing sound, acoustic related vehicle information, or interference sound information (e.g., data indicating window position, engine rotations per minute (RPM), vehicle speed, heating ventilation and cooling (HVAC) system fan setting(s), audio level, or other parameters); external sound measurements; and other information to enhance speech recognition, prompting using, for example, spoken dialogue, dialogue control, and/or other spoken dialogue systems or methods. Prompting may, for example, be information, speech, or other audio signals output to a user from a spoken dialogue system. Sound or acoustic related vehicle information may not in itself include sound signals. For example, sound or acoustic related information may represent (e.g., include information on) an engine RPM, but not a signal representing the sound the engine makes. Sound or acoustic related information may represent (e.g., include information on) the fact that a window is open (or open a certain amount), but not a signal representing the sound the wind makes through the open window. Sound related vehicle information may represent or include vehicle parameters, describing the state of the vehicle or vehicle systems. 
     Sound related vehicle information or signals or information related to the operation of vehicle systems producing or causing sound may be used to generate an interference profile record (IPR). An interference profile record may, for example, include noise or sound type parameters, noise level or sound intensity parameters, and other information. (In some embodiments, sound related vehicle information may include noise type parameters and/or noise level parameters.) Noise type parameters may, for example, represent or be based on a type of sound related vehicle information (e.g., engine RPM, HVAC fan setting(s), window position, audio playback level, vehicle speed, or other information) or combinations of types of sound related vehicle information. For example, a noise type parameter may include an indication of whether or not or how much a window is open (but not include a signal representing the sound of wind). Noise level parameters may represent the level of intensity of sound related vehicle information (e.g., HVAC fan setting high, medium, low, or off; audio playback level high, medium, low or off; or other sound related vehicle information) or combinations of sound related vehicle information (e.g., open windows and speed above threshold speed may be represented as noise type parameter of wind and noise level parameter of high). For example, a noise level parameter may include an indication of whether or not or how much a fan is running (but not include a signal representing the sound of the fan). Interference profile records may, in some embodiments, be or may include an integer (e.g., an 8-bit integer or other type of integer), a percentage, a range of values, or other data or information. 
     In some embodiments, interference profile records (e.g., noise type parameters, noise level parameters and/or other parameters) may be used to enhance speech recognition. The interference profile record may, for example, be used by a speech recognition system or process (e.g., including a signal processor, automatic speech recognition (ASR) system, or other system(s) or method(s)) to modify or alter a sound signal to improve speech recognition system or process decoding. In one example, a signal processor, ASR, or other system may, based on interference profile records (e.g., noise type parameters and noise level parameters), apply a pre-trained filter (e.g., a Weiner filter, comb filter, or other electronic signal filter) to modify or alter the input signal to limit or remove noise and improve speech recognition. For example, based on noise type parameters a type of pre-trained filter may be applied, and based on noise level parameters filter settings or parameters may be determined and/or applied. Filter settings or parameters may, for example, control or represent an amount or level or filtering, frequencies filtered, or other attributes of a filter. A level of filtering (e.g., an amount of filtering), frequencies filtered, and other attributes of filter may, for example, be based on noise level parameters, which may represent a window position (e.g., a percentage of how far window is open), engine revolutions per minute (RPM), vehicle speed, environmental control fan setting, audio playback level, or other vehicle parameters. For example, if a noise level parameter indicates a high level of noise rather than a low level of noise, a higher level or amount of filtering rather than a lower level may be applied to the input signal. Different combinations of filtering levels and noise level parameters may of course be used. Other signal processing methods and/or modules may be used. 
     In one example, an ASR or other system may, based on interference profile records (e.g., noise type parameters and noise level parameters), apply a pre-trained acoustic model to improve speech recognition. A type of pre-trained acoustic model (e.g., among multiple acoustic models) may be chosen based on interference profile records (e.g., noise type parameters, noise level parameters, and/or other parameters). In some embodiments, a type of acoustic model may correspond to one or more interference profile records. For example, a predetermined acoustic model may be used if predetermined interference profile records are generated based on sound related vehicle information. 
     According to some embodiments, modification of a speech recognition process based on interference profile records may be adapted. In an adaptation operation supervised learning may be used to adapt or change signal modification parameters (e.g., filter parameters or other parameters), adapt or train acoustic model transformation matrices, adapt or change which pre-trained acoustic model is used, or adapt other features of spoken dialogue system. In an adaptation operation, the effect of signal modification parameters may, for example, be monitored or measured by determining the success or effectiveness of an ASR or other components of a speech recognition system in identifying speech (e.g., words, phrases, and other parts of speech). Based on the measurements, signal modification parameters may, for example, be adapted or changed to improve the function or success of speech recognition and the spoken dialogue system. In one example, a predefined filter (e.g., a Weiner filter, comb filter, or other filter) operating with a given set of filter parameters may be applied based on a given set of noise type parameters and noise level parameters. An adaptation module may, for example, measure how effective or successful a filter operating with a given set of parameters based on noise type parameters and noise level parameters is in enhancing or improving speech recognition. Based on the measurement, the filter parameters may be adapted or changed to improve or enhance speech recognition. Other signal modification parameters may be adapted. 
     In some embodiments, interference profile records (e.g., noise type parameters, noise level parameters, and/or other parameters) may be used by text to speech, audio processing, or other modules or methods to enhance speech prompting or spoken dialogue, audio output, or other audio signal output, typically to passengers. An audio processing module or other system may, for example, based on noise type parameters, noise level parameters, and/or other parameters increase or decrease a prompt level, shape or reshape the prompt spectrum, modify prompt pitch, or otherwise alter a prompt. An audio processing module may, for example, increase audio output volume level, shape or reshape an audio spectrum (e.g., audio playback spectrum), modify audio playback pitch, and/or otherwise alter audio or sounds. A text to speech module or other system may, for example, modify or alter speech rate, syllable duration, or other speech related parameters based on noise type parameters, noise level parameters, and/or other parameters. 
     According to some embodiments, modification of speech prompting, audio output, or other audio signal output based on interference profile records may be adapted. In an adaptation operation supervised learning may be used to adapt or change parameters associated with increasing or decreasing a prompt level, parameters used to shape or reshape prompt spectrum, parameters used to modify prompt pitch, and/or other parameters. In an adaptation operation, the effect of parameters used to increase or decrease a prompt level, parameters used to reshape prompt spectrum, parameters used to modify prompt pitch, and/or other parameters may be measured. The substance or content of speech or audio prompts may be altered. Based on the measurement, the parameters used to increase or decrease a prompt level, parameters used to reshape prompt spectrum, parameters used to modify prompt pitch, and/or other parameters may be adapted or changed to improve or enhance prompting or audio output function. 
     In some embodiments, interference profile records (e.g., noise type parameters, noise level parameters, and/or other parameters) may, for example, be used by a dialogue control module or other system or method to enhance vehicle occupant interaction with the spoken dialogue system. A spoken dialogue control module or other system may, for example, based on noise type parameters, noise level parameters, and/or other parameters modify dialogue control, introduce prompts (e.g., introductory prompts), modify audio prompts, modify the substance or content of output speech, modify dialogue style, listen and respond to user confusion, modify multi-modal dialogue, modify back-end application functionality, and/or perform other operations. 
     According to some embodiments, modification of spoken dialogue control based on interference profile records may be adapted. In an adaptation operation, supervised learning may be used to adapt or change parameters used in dialogue control, prompt introduction, prompt modification, dialogue style modification, user confusion response, multi-modal dialogue modification, back-end application functionality modification, and/or other operations. In an adaptation operation, the effect of parameters used in dialogue control, prompt introduction, prompt modification, dialogue style modification, user confusion response, multi-modal dialogue modification, back-end application functionality modification, and/or other operations may be measured. Based on the measurement, the parameters used in dialogue control, prompt introduction, prompt modification, dialogue style modification, user confusion response, multi-modal dialogue modification, back-end application functionality modification, and/or other operations may be adapted or changed to improve or enhance spoken dialogue system function. 
     A spoken dialogue system or method according to embodiments of the present invention may be particularly useful by modifying or altering automatic speech recognition, audio prompting, dialogue control and/or other operations based on accurate timed or real-time vehicle sound related information, a-priori understanding of noise characteristics, and other information. Additionally, parameters used to modify or alter automatic speech recognition, prompting, dialogue control and/or other operations may be adapted or changed to improve the function of the spoken dialogue system throughout the life of the spoken dialogue system. Other and different benefits may realized by embodiments of the present invention. 
       FIG. 1  is a schematic illustration of vehicle with an automatic speech recognition system according to an embodiment of the present invention. A vehicle  10  (e.g., a car, truck, or another vehicle) may include or be connected to a spoken dialogue system  100 . One or more microphone(s)  20  may be associated with system  100 , and microphones  20  may receive or record speech, ambient noise, vehicle noise, audio signals and other sounds. Microphones  20  may be located inside vehicle cabin  22 , exterior to vehicle cabin  22 , or in another location. For example, one microphone  20  may be located inside vehicle cabin  22  and may receive or record speech, non-speech related sounds, noise, and/or sounds inside the cabin  22 . Non-speech related sounds may include for example vehicle  10  related noises (e.g., engine noise, heating ventilation and cooling (HVAC) system noise, etc.), non-vehicle related noise (e.g., noises from outside the vehicle), audio system sounds (e.g., music, radio related sounds), and other sounds. One or more exterior microphone(s)  24  may, for example, be located exterior to vehicle cabin  22  (e.g., on vehicle body, bumper, trunk, windshield or another location). 
     One or more sensors may be attached to or associated with the vehicle  10 . A window position sensor  60 , engine rotation per minute (RPM) sensor  26 , vehicle speed sensor  28  (e.g., speedometer), HVAC sensor  30  (e.g., HVAC fan setting sensor), audio level sensor  32  (e.g., audio system volume level), exterior microphones  24 , and other or different sensors such as windshield wiper sensors may measure sound related vehicle information, vehicle parameters, vehicle conditions, noise outside vehicle, or vehicle related information. Sound related vehicle information or interference sound information may be transferred to system  100  via, for example, a wire link  50  (e.g., data bus, a controller area network (CAN) bus, Flexray, Ethernet) or a wireless link. The sound related vehicle information may be used by system  100  or another system to determine an interference profile record (e.g., noise profile record) or other data representing the sound related vehicle information. Other or different sensors or information may be used. 
     In one embodiment of the present invention, spoken dialogue system  100  may be or may include a computing device mounted on the dashboard or in a control console of the vehicle, in passenger compartment  22 , or in the trunk. In alternate embodiments, spoken dialogue system  100  may be located in another part of the vehicle, may be located in multiple parts of the vehicle, or may have all or part of its functionality remotely located (e.g., in a remote server or in a portable computing device such as a cellular telephone). Spoken dialogue system  100  may, for example, perform one or more of outputting spoken dialogue or audio prompts to vehicle occupants and inputting audio information representing speech from vehicle occupants. 
     According to some embodiments, a speaker, loudspeaker, electro-acoustic transducer, headphones, or other device  40  may output, broadcast, or transmit audio prompts or spoken dialogue responses to voice commands, voice responses, audio commands, audio alerts, requests for information, or other audio signals. Audio prompts and/or responses to voice commands may, for example, be output in response to speech commands, requests, or answers from a vehicle passenger. A prompt may, for example, include information regarding system  100  functionality, vehicle functionality, question(s) requesting information from a user (e.g., a vehicle passenger), information requested by a user, or other information. Prompts and speech input may, in some embodiments, be used in a vehicle in other manners. 
     A display, screen, or other image or video output device  42  may, in some embodiments, output information, alerts, video, images or other data to occupants in vehicle  10 . Information displayed on display  42  may, for example, be displayed in response to requests for information by driver or other occupants in vehicle  10 . 
     Vehicle  10  may, in some embodiments, include input devices or area(s)  44  separate from or associated with microphones  20 . Input devices or tactile devices  44  may be, for example, touchscreens, keyboards, pointer devices, turn signals or other devices. Input devices  44  may, for example, be used to enable, disable, or adjust settings of spoken dialogue system  100 . 
     While various sensors and inputs are discussed, in certain embodiments only a subset (e.g. one or another number) of sensor(s) or input may be used. 
       FIG. 2  is a schematic illustration of a spoken dialogue system according to embodiments of the present invention. Spoken dialogue system  100  may include one or more processor(s) or controller(s)  110 , memory  120 , long term storage  130 , input device(s) or area(s)  44 , and output device(s) or area(s)  42 . Input device(s) or area(s)  140  and output device(s) or area(s)  150  may be combined into, for example, a touch screen display and input which may be part of system  100 . 
     System  100  may include one or more databases  150 , which may include, for example, sound or acoustic related vehicle information  160  (e.g., interference sound information), interference profile records (IPRs)  180 , spoken dialogue system ontologies  170 , and other information. Sound related vehicle information  160  may, for example, include vehicle parameters, recorded sounds, and/or other information. Databases  150  may, for example, include interference profile records  180  (e.g., noise type parameters, noise level parameters, and/or other information), noise profiles, noise profile records, and/or other data representing the vehicle parameters and/or other information. Databases  150  may be stored all or partly in one or both of memory  120 , long term storage  130 , or another device. 
     Processor or controller  110  may be, for example, a central processing unit (CPU), a chip or any suitable computing or computational device. Processor or controller  110  may include multiple processors, and may include general-purpose processors and/or dedicated processors such as graphics processing chips. Processor  110  may execute code or instructions, for example, stored in memory  120  or long-term storage  130 , to carry out embodiments of the present invention. 
     Memory  120  may be or may include, for example, a Random Access Memory (RAM), a read only memory (ROM), a Dynamic RAM (DRAM), a Synchronous DRAM (SD-RAM), a double data rate (DDR) memory chip, a Flash memory, a volatile memory, a non-volatile memory, a cache memory, a buffer, a short term memory unit, a long term memory unit, or other suitable memory units or storage units. Memory  120  may be or may include multiple memory units. 
     Long term storage  130  may be or may include, for example, a hard disk drive, a floppy disk drive, a Compact Disk (CD) drive, a CD-Recordable (CD-R) drive, a universal serial bus (USB) device or other suitable removable and/or fixed storage unit, and may include multiple or a combination of such units. 
       FIG. 3  is a block diagram of a spoken dialogue system according to embodiments of the present invention. The system of  FIG. 3  may, for example, part of the system of  FIG. 2 , or of other systems, and may have its functionality executed by the system of  FIG. 2 , or by other systems. The components of the system of  FIG. 3  may, for example, be dedicated hardware components, or may be all or in part code executed by processor  110 . Microphone  20  or another input device may receive, record or measure sounds, noise, and/or speech in vehicle. The sounds may include speech, speech commands, verbal commands, or other expression from an occupant in vehicle  10 . Microphone  20  may transmit or transfer an audio signal or signal  200  representing the input sounds, including speech command(s), to system  100 , speech recognition system or process  201 , or other module or system. Speech recognition system or process  201  may, for example, include a signal processor  202  (e.g., speech recognition front-end), speech recognition module  204 , and other systems or modules. Audio signal  200  representing the input sounds, including speech command(s) may be output to an automatic speech recognition system  201 , a signal processor or signal processing or enhancement device  202  associated with system  100 , an adaptation module, or other device. Signal processor  202  may, for example, receive the audio signal. Signal processor  202  may, for example, filter, amplify digitize, or otherwise transform the signal  200 . Signal processor  202  may transmit the signal  200  to a speech recognition module or device  204 . Automatic speech recognition (ASR) module or speech recognition module  204  may extract, identify, or determine words, phrases, language, phoneme, or sound patterns from the signal  200 . Words may be extracted by, for example, comparing the audio signal to acoustic models, lists, or databases of known words, phonemes, and/or phrases. Based on the comparison, potential identified words or phrases may be ranked based on highest likelihood and/or probability of a match. ASR module  204  may output or transmit a signal  200  representing identified words or phrases to a semantic interpreter  206 . 
     According to some embodiments, a vehicle occupant may enter a command or information into an input device  44 . Input device  44  may transmit or output a signal representing the command or information to tactile input recognition module  208 . Tactile input recognition module  208  may identify, decode, extract, or determine words, phrases, language, or phoneme in or from the signal. Tactile input recognition module  208  may, for example, identify words, phrases, language, or phonemes in the signal by comparing the signal from input  44  to statistical models, databases, dictionaries or lists of words, phrases, language, or phonemes. Tactile input recognition module  208  may output or transfer a signal representing identified words or phrases to semantic interpreter  206 . The tactile signal may, for example, be combined with or compared to signal  200  from ASR module  204  in semantic interpreter  206 . 
     According to some embodiments, semantic interpreter  206  may determine meaning from the words, phrases, language, or phoneme in the signal output from ASR module  204 , tactile input recognition module  208  and/or another device or module. Semantic interpreter  206  may, for example, be a parser (e.g., a semantic parser). Semantic interpreter  206  may, for example, map a recognized word string to dialogue acts, which may represent meaning. Dialogue acts may, for example, refer to the ontology of an application (e.g., components of an application ontology). For example, user may provide a speech command or word string (e.g., “Find me a hotel,”) and semantic interpreter  206  may parse or map the word string into a dialogue act (e.g., inform(type=hotel)). Semantic interpreter  206  may, for example, use a model that relates words to the application ontology (e.g., dialogue acts in application ontology). The model may, for example, be included in speech recognition grammar (e.g., in database  150 , memory  120 , or other location) and/or other locations. Speech recognition module  204  may identify the words in the statement and transmit a signal representing the words to semantic interpreter  206 . Dialogue acts, information representing spoken commands, and/or other information or signals may be output to a dialog control module  210 . 
     Dialog control module  210  may, in some embodiments, generate, calculate or determine a response to the dialogue acts. For example, if a dialogue act is a request for information (e.g., inform(type=hotel)), dialog control module  210  may determine a response to the request providing information (e.g., a location of a hotel), a response requesting further information (e.g., “what is your price range?”), or other response. Dialog control module  210  may function in conjunction with or be associated with a backend application  212 . A backend application  212  may, for example, be a data search (e.g., search engine), navigation, stereo or radio control, musical retrieval, or other type of application. 
     According to some embodiments, a response generator or response generation module  214  may, for example, receive response information from dialog control module  210 . Response generation module  214  may, for example, formulate or generate text, phrasing, or wording (e.g., formulate a sentence) for the response to be output to a vehicle occupant. 
     A visual rendering module  216  may generate an image, series of images, or video displaying the text response output by response generation module  214 . Visual rendering module  216  may output the image, series of images, or video to displays  44  or other devices. 
     A text to speech module  218  may convert the text from response generation module  214  to speech, audio signal output, or audible signal output. The speech signal may be output from text to speech module  218  to audio signal processor  220 . Audio signal processor  220  may convert the signal from digital to audio, amplify the signal, uncompress the signal, and/or other modify or transform the signal. The audio signal may be output to speakers  40 . Speakers  40  may broadcast the response to the vehicle occupants. 
     An interference profile module  222  may receive sound related vehicle information  160 , vehicle parameters, received sound signals, and/or other information representing one or more sounds from data bus  50  or other sources. In some embodiments, data bus  50  may transmit or transfer sound related vehicle information  160  to interference profile module  222  associated with spoken dialogue system  100  or another module or device associated with system  100 . 
     Interference profile records (IPR)  180  may be generated, determined, or calculated by interference profile module  222  based on the sound related vehicle information  160 . Interference profile records  180  may include noise level parameters (e.g., sound intensity parameters), noise or sound type parameters, and/or other information. Noise level parameters, noise type parameters, and/or other parameters may be determined based on sound related vehicle information  160 , received sounds, and/or other information representing sounds or noise. For example, sound related vehicle information  160  may indicate or represent that a heating, ventilation, and air condition (HVAC) system fan is on and operating at a high setting. An IPR  180  including a noise type parameter of fan (e.g., noise type=fan) and a noise level parameter of high (e.g., noise level=high) may, for example, be generated to represent sound related vehicle information  160  indicated an HVAC fan is on a setting of high. Other IPR&#39;s  180  including noise type parameters, noise level parameters, and other parameters may be generated. Noise level parameters and noise type parameters may represent a noise or sound in a vehicle or the likely presence of a noise or sound in vehicle, but typically do not include audio signals or recordings of the actual noise or sound. 
     According to some embodiments, modification module or steps  224  may, based on the noise level parameters, noise type parameters, and/or other parameters alter or modify the audio signal  200 , filter noise, and/or otherwise modify automated speech recognition. Modification module  224  may, in some embodiments, modify an audio signal  200  by applying a filter to audio signal  200 , determining an acoustic model to be used in speech recognition, and/or otherwise enhancing signal processing  202 , speech recognition  204 , or speech recognition steps or processes. 
     According to some embodiments, an interference profile record may, for example, be used by text to speech  218 , audio processing  220 , or other modules or methods to enhance audio speech prompting, audio output, or other sounds or broadcasts output from system  100 . Text to speech  218  parameters or output may be modified (e.g., by modification module  224 ) by increasing or decreasing speech rate, increasing or decreasing syllable duration, and/or otherwise modifying speech output from system  100  (e.g., via speaker  40 ). Parameters associated with audio processing  220  (e.g., prompt level, prompt spectrum, audio playback, or other parameters) may be modified based on an interference profile record (e.g., noise type parameters, noise level parameters, and other parameters). Audio output from system may, for example, be modified by increasing prompt level (e.g., volume), altering prompt pitch, shaping or reshaping a prompt spectrum (e.g., to increase signal to noise ratio), enhancing audio playback (e.g., stereo playback), and/or otherwise enhancing or altering audio output from system  100  (e.g., via speaker  40 ). 
     A combination of text to speech  218 , audio processing  220 , and/or other types speech prompting or audio output modification  224  may be used. For example, Lombard style or other type of speech modification may be used. Lombard style modification may, for example, model human speech in a loud environment, an environment with background noise, or in a setting where communication may be difficult. Lombard style modification may, for example, modify audio spectrum, pitch, speech rate, syllable duration and other audio characteristics using audio processing  220 , text to speech  218 , or other modules and/or operations. 
     According to some embodiments, based on the noise level parameters, noise type parameters, and/or other parameters dialogue control  210  or other systems or processes associated with spoken dialogue system  100  may be modified and/or altered. Dialogue control  210  may, for example, be modified or altered (e.g., by modification module  224 ) by implementing or imposing clarification acts (e.g., asking a user for explicit confirmation of input, to repeat input, or other clarifications), determining and outputting introductory audio prompts (e.g., prompting user using output speech that voice recognition may be difficult with windows down, high engine RPM, or based on other vehicle parameter(s)), modifying prompts (e.g., controlling the pace or timing of prompts), modifying dialogue style (e.g., prompting user for single slot or simple information rather than complex information, enforcing exact phrasing, avoiding mixed initiative and other modifications), monitoring and responding to user confusion, and/or otherwise modifying dialogue control  210 . In some embodiments, multi-modal dialogue (e.g., spoken dialogue combined with tactile, visual, or other dialogue) may, for example, be modified (e.g., by modification module  224 ). Multi-modal dialogue may, for example, be modified by reverting to, weighting, or favoring visual display over speech prompting, by reverting to visual display of system hypotheses (e.g., questions, requests for information, and other prompts), prompting or requesting tactile confirmation from a user (e.g., prompting a user to select a response from list of responses displayed on touchscreen or other output device), encouraging use of tactile modality (e.g., reducing confidence levels associated with semantic interpreter  206 ), switching from speech based to other modalities for a subset of application functions (e.g., simple command and control by tactile means), or other modifications. Back-end application functionality may be modified (e.g., by modification module  224 ) based on the interference profile records. For example, functionality of back-end application services or features may be locked out, reduced, or otherwise modified (e.g., lock out voice search, allow radio control, and other services). 
       FIG. 4  is a block diagram of an automatic speech recognition system according to embodiments of the present invention. According to some embodiments, an interference profile module  222  may receive sound related vehicle information  160  including or representing, for example, vehicle parameters and other information from a data bus  50 . Vehicle parameters may, for example, include window position (e.g., open or closed, open a certain amount, etc.), engine settings (e.g., engine revolutions per minute (RPM)), vehicle speed, HVAC fan settings (e.g., off, low, medium, high), audio playback levels, or other vehicle related parameters. According to some embodiments, interference profile module  222  may receive sound related vehicle information  160  from microphones (e.g., exterior microphones  24 , interior microphones  20 , or other microphones). Sound related vehicle information  160  from microphones may, in some embodiments, include non-speech related sounds, vehicle related sounds, non-vehicle related sounds, infrastructure sounds, wind noise, road noise, speech from people outside vehicle cabin, environmental sounds, Interference module  222  may, for example, based on sound related vehicle information  160  generate interference profile records (IPR)  180 . 
     Interference profile records  180  may, for example, be a table, data set, database, or other set of information. Each IPR  180  may, for example, be a representation of sound related vehicle information  160  (e.g., vehicle parameters and other sounds or information). An IPR  180  may, for example, include a noise level parameter  304  (e.g., sound intensity parameter), noise type parameter  306  (e.g., sound type parameter or noise classification parameter), and other parameters representing sound related vehicle information  160 . In some embodiments, noise level parameter  304 , noise type parameter  306 , and other parameters may represent a combination of categories of sound related vehicle information  160  (e.g., vehicle parameters, received sounds, and/or other sounds or information). An IPR  180  including noise level parameters  304 , noise type parameters  306 , and/or other parameters may, for example, represent vehicle parameters (e.g., engine RPM, HVAC fan setting, window position, etc.) or vehicle related sounds in real-time, continuously, or over a predetermined period of time. Interference profile records  180  may, for example, be generated continuously, in real-time when spoken dialogue system  100  is activated, any time vehicle is powered on, or at other times. 
     Noise type parameter  306  may, for example, be a classification, categorization, label, tag, or information representing or derived from sound related vehicle information  160  including vehicle parameters (e.g., engine RPM, window position, HVAC fan setting, vehicle speed, audio playback level, and other parameters) and/or other information. Noise or sound type parameters  306  may, for example, be determined, generated or assigned based on signals (e.g., sound related vehicle information  160 ) received from CAN bus  50 . Signals received from CAN bus  50  may, for example, represent or include sound related vehicle information  160 , which may represent vehicle parameters (e.g., vehicle window position, engine RPM, vehicle speed, HVAC fan setting, audio playback level, and other parameters) and/or other information. Noise type parameters  306  may, for example, represent a vehicle parameter, pre-defined combinations of vehicle parameters, or other information received from CAN bus  50 . For example, if a signal is received from CAN bus  50  indicating engine RPM is higher than a threshold RPM value a noise type parameter  306  of engine (e.g., noise_type=Engine) may be generated or assigned. For example, a signal received via CAN bus  50  indicating that an HVAC system is at a certain setting may result in the generation or assignment of a noise or sound type parameter  306  of fan (e.g., noise_type=fan). For example, sound related vehicle information  160  indicating a window is open may result in the assignment of a noise type parameter  306  window (e.g., noise_type=window). Other noise type parameter  306  determinations, assignments, and classifications may be used. 
     Noise level parameters  304  may, for example, be derived from vehicle parameters including (e.g., fan dial or input setting, HVAC system setting, engine RPM, vehicle speed, audio playback level, and/or other vehicle parameters). Noise level parameters  304  may, for example, be a representation of sound level (e.g., the decibel (dB) level of the sound) or other measure of sound level or feature. Noise level parameters  304  may, for example, be low, medium, high or other parameters and may represent or quantify ranges of sound intensity. 
     Interference profile records  180  (e.g., noise level parameters  304  and noise type parameters  306 ) may, in some embodiments, be determined, generated, or calculated using logic (e.g., using metrics or thresholds), mathematical approaches, a table (e.g., a look-up table), or other operations. For example, if sound related vehicle information  160  indicates engine RPM is above a predefined threshold, a noise type parameter  306  of engine (e.g., noise_type=engine) and noise level parameter  304  of high (e.g., noise_level=high) may be determined or generated. For example, if vehicle parameters from data bus indicate an HVAC fan is on a high setting, a noise type parameter  306  equal to fan (e.g., noise_type=fan), noise level parameter  304  of high (e.g., noise_level=high), and/or other parameters may be assigned. Other operations may be used. Typically, a noise type parameter is a discrete parameter selected among a list, e.g., engine, window open, fan, wind, audio, audio, etc. However, other noise type parameters may be used. A noise type parameter and noise level parameter typically does not include a sound recording or other direct information regarding the actual noise produced. 
     In some embodiments, combinations of multiple types of sound related vehicle information  160  (e.g., vehicle parameters, measured sounds, and other sounds or information) may, in some embodiments, be used in logic operations and/or other mathematical operations to determine or calculate interference profile records  180  (e.g., noise level parameters  304  and noise type parameters  306 ). For example, if sound related vehicle information  160  from data bus indicates vehicle speed is greater than a threshold speed (e.g., 70 miles per hour (mph) or another speed) and window position is beyond a threshold (e.g., more than 25% open or another threshold), a noise level parameter  304  of high (e.g., noise_level=high) and noise type parameter  306  equal to wind (e.g., noise_type=wind) may be determined, assigned, or generated. Other thresholds and parameters may be used. 
     Interference profile records  180  may, in some embodiments, be determined, generated, or calculated using quantization or other operations. Sound related vehicle information  160 , vehicle parameters, measured sounds, or other information may, for example, be quantized to determine noise level parameter  304  values and noise type parameter  306  values. For example, engine RPM values may be quantized to an 8 bit or other size integer noise level parameter  304  values. Noise level parameter  304  (e.g., 8 bit integer representing engine noise) may, for example, include information about engine characteristics (e.g., engine fundamental frequencies and harmonics). Audio playback levels, for example, may be quantized to 8 bit or other size integers. Each 8 bit integer may, for example, represent an interference profile record  180  (e.g., a noise level parameter  304 ). Other quantization steps may be used. 
     According to some embodiments, modification module or processes  224  may, based on interference profile records  180 , modify audio signals  200 , filter noise, and improve spoken dialogue system  100  function. Modification module or processes  224  may, in some embodiments, modify an audio signal  200 , filter noise, modify features of an audio signal  200 , and/or otherwise alter an audio signal  200  independent of speech recognition device  300  (e.g., prior to speech recognition  204 ), dependent on speech recognition  302  (e.g., during speech recognition  204  using, for example, ASR front end  314 ), or during other steps or process. 
     In some embodiments, an audio signal  200  (e.g., output from microphone  20 ) may be modified, filtered, or altered independent  300  of or before being received in speech recognition module  204 . System  100  may, for example, include multiple filters  312  (e.g., Weiner filters, comb filters, analog, digital, passive, active, discrete-time, continuous time, and other types of filters) and each filter  312  may include filter parameters  320 . Filters  312  may, for example, be stored in memory  120 , database  150 , long-term storage  130 , or a similar storage device. Each filter  312  and filter parameters  320  may, for example, function best to filter certain noise level parameters  304  and noise type parameters  306 . Audio signal  200  may, for example, be modified and/or altered during signal processing  202 . Audio signal  200  may be modified during signal processing  202  based on interference profile records  180  (e.g., noise type parameters  306  and noise level parameters  304 ). Based on noise type parameters  306 , modification module  310  may, for example, determine a filter  312  (e.g., a Weiner filter, comb filter, low pass filter, high pass filter, band pass filter, or other type of filter) or other module or device to filter, limit, or reduce interference noise. Filter parameters  322  (e.g., frequencies, amplitude, harmonics, tunings, or other parameters) may, for example, be determined based on noise level parameters  304 . Filter  312  may be applied to an input signal, audio signal  200 , or other type of signal in signal processor  202  or in another module or step. 
     According to some embodiments, if IPRs  180  indicate wind noise (e.g., noise_type=wind) may be present, a filter  312  (e.g., Weiner filter) may be applied by signal processor  202  to filter or reduce wind noise in the audio signal  200 . Weiner filter parameters  320  may, in some embodiments, be determined based on noise level parameters  304  (e.g., noise_level=high, medium, low, or off), noise type parameters  306 , and other parameters. For example, modification module  224  may include predetermined Weiner filter parameters  320  to apply during signal processing  202  based on a given noise level parameter  304 . After application of filter  312  (e.g., Weiner filter), audio signal  200  may, for example, be output to automated speech recognition (ASR) module  204  with reduced or limited wind noise in the signal. 
     According to some embodiments, if IPR&#39;s  180  indicate engine noise (e.g., noise_type=engine) may be present, a time varying comb filter  312  may be applied during signal processing  202  to filter out engine noise. Time varying comb filter  312  parameters may, for example, be determined based on noise level parameter  304  (e.g., 8 bit integers representing engine noise). Noise level parameter  304  (e.g., 8 bit integer representing engine noise) may, for example, include information about engine characteristics (e.g., engine fundamental frequencies and harmonics). Based on noise level parameter  304 , time varying comb filter  312  parameters may, for example, be determined Time varying comb filter parameters  322  may, for example, be determined such that comb filter is aligned with fundamental frequencies and harmonics in the engine noise portion of audio signal  200 . Time varying comb filter with parameters  322  aligned with fundamental frequencies and harmonics in the engine noise portion of an audio signal  200  may attenuate or reduce the intensity of engine fundamental frequencies and harmonics in an audio signal  200  transform (e.g. a signal Fourier transform). A signal  200  with attenuated or reduced fundamental engine frequencies and amplitudes may, for example, be output to an automated speech recognition decoder  316 . Automated speech recognition decoder  316  may interpret speech, commands, or other information in the audio signal  200 . 
     According to some embodiments, success of speech recognition modification based on the noise type parameters and the noise level parameters in increasing speech recognition functionality may be measured. Based on the measure success speech recognition modification may be adapted (e.g., during a learning or supervised learning operation). 
     According to some embodiments, filter parameters  322  (e.g., Weiner filter, comb filter, etc.) used with given interference profile records  180  (e.g., noise type parameters  306  and noise level parameters  304 ) may be defined during manufacturing, during an adaptation process  320  (e.g. a learning or supervised learning operation), or at another time. Filter parameters  322  may, for example, be determined such that filter  312  is most effective in removing noise from an audio signal  200 . During an adaptation process  320 , a signal  200  and IPR(s)  180  associated with signal  200  may be received at system  100  (e.g., at an adaptation module  320 ). Signal  200  may, for example, include speech, noise, and possibly other sounds. Interference profile record(s)  180  associated with signal  200  may, for example, be output from data bus  50  concurrently with or at roughly the same time as signal  200  is received. An adaptation module  320  may, for example, measure how effective filter parameters  322  (e.g., derived from or determined based on IPRs  180 ) are in removing noise from signal  200  by comparing signal  200  to a signal output from filter  312  (e.g., operating with predefined filter parameters  320 ) or using on other methods. The success or filter parameters  322  in improving speech recognition may me be measured using other approaches and/or metrics. Adaptation module  320  may based on the measurement change or adapt filter parameters  322  to more effectively remove noise from signals  200  associated with a given IPR  180  (e.g., given noise type parameters  306  and noise level parameters  304 ). Adaptation steps  320  may, for example, be performed while vehicle is driven by a driver or at other times and filter parameters  322  may be adapted based on the supervised learning or other methods. 
     For example, during an adaptation process  320  a vehicle may be driven above a predefined threshold speed with the windows open and a noise level parameter  304  of high and noise type parameter  306  of wind (e.g., noise_type=wind) may be generated. Signals  200  including speech and other noise (e.g., vehicle related noises) may be received at system  100  (e.g., from microphone  20 ) during adaptation operation  320 . An adaptation module  320  may, for example, measure how effective filter parameters  322  (e.g., based on noise type parameters  306  and noise level parameters  304 ) are in removing noise from signal  200 . In some embodiments, how effective filter parameters  322  are in removing noise from signal  200  may be measured by comparing signal  200  to a signal output from filter  312  (e.g., operating with predefined filter parameters  320 ) or using other methods. Filter parameters  322  associated with noise type parameters  306  and noise level parameters  304  may, for example, be adapted or changed to more effectively filter or remove noise from signal  200 . Filter parameters  322  associated with noise type parameters  306  and noise level parameters  304  may, in some embodiments, not be changed or adapted if filter parameters  322  as measured are effective or successful in removing noise from signal. Success or effectiveness of filter parameters  322  may, for example, be determined by evaluating the performance or function of speech recognition  204  given filter parameters  322 . Other approaches and metrics may be used. 
     According to some embodiments, modification module  310  may modify an audio signal  200  within modules and/or devices in speech recognition module  204 . Audio signal may  200 , for example, be received from microphone  20  or similar device and may include speech from vehicle occupants (e.g., passengers, drivers, etc.) and other sounds (e.g., background noise, vehicle related sounds, and other sounds). Speech recognition module  204  may, for example, include an automatic speech recognition (ASR) front-end  314 . Based on IPR&#39;s  180  signals may be modified at ASR front end  314  to filter out noise (e.g., wind noise, engine noise or another type of noise) or to otherwise modify audio signal  200 . A filter  312  (e.g., a Weiner filter) may, for example, be applied to signal  200  in ASR front-end  314  to filter wind noise from an audio signal  200 . The type of filter  312  and filter parameters  322  may be determined based on noise type parameter  306  and noise level parameter  304 . For example, a vehicle  10  may travel at a speed above a threshold speed with windows open and noise type parameter  306  wind and noise level parameter  304  of high may be generated. Based on the noise type parameter  306  of wind and noise level parameter  304  of high, a filter  312  (e.g., a Weiner filter) with predefined filter parameters  322  may be applied to signal  200  in ASR front-end  314 . 
     According to some embodiments, automatic speech recognition module  204  may include acoustic models  318 . A specific previously generated acoustic model among multiple acoustic models  318  may be chosen during sound analysis to decode speech, the model being chosen depending on, for example, interference profile records  180  (e.g., noise level parameters  304  and/or noise type parameters  306 ). Acoustic models  318  may be or may include statistical models (e.g., Hidden Markov Model (HMM) statistical models or another statistical models) representing the relationship between phonemes, sounds, words, phrases or other elements of speech and their associated or representative waveforms. 
     According to some embodiments, IPR&#39;s  180  (e.g., noise level parameters  304 , noise type parameters  306 , or other parameters) may be used to determine, choose or select which acoustic model  318  to use in a speech recognition operation. For example, an IPR  180  (e.g., noise level parameter  304  of high and noise type parameter  306  of window) may indicate high window noise in a signal. Modification module  310  may based on IPR  180  indicating high window noise, select or determine an acoustic model  318  among several acoustic models  318  that is best suited to decoding speech in a signal with high window noise. 
     Acoustic models  318  may, for example, be adapted, trained or generated from speech samples during an adaptation operation  320 , manufacturing, testing, or at another time. Acoustic models  318  may, for example, be adapted during adaptation operation  320  (e.g., a supervised learning operation) based on noise level parameters  304  and the noise type parameters  306 . An adaptation module  320  may, for example, measure how effective an acoustic model  322  (e.g., determined based on IPRs  180 ) is in decoding speech from signal  200 . The success of an acoustic model  322  (e.g., including predefined acoustic model transformation matrices) in improving speech recognition may be measured and an acoustic model  322  may be adapted based on the measurement. Acoustic model  322  may, for example, be adapted using maximum likelihood linear regression or other mathematical approaches to adapt or train acoustic model transformation matrices used in conjunction with predefined noise type parameters  306  and noise level parameters  304 . 
     For example, during an adaptation or training operation vehicle  10  may be driven above a threshold speed with windows open. A noise level parameter  304  of high and noise type parameter  306  of wind (e.g., noise_type=wind) may be generated and output to adaptation module  320 . Speech and other noise may be recorded (e.g., by microphone  20 ) and a signal  200  including speech may be output to adaptation module  320 . The success of acoustic model  318  in decoding speech based on the noise type parameter  306  of wind (e.g., noise_type=wind) and noise level parameter  304  of high (e.g., noise_level=high) may be measured. Based on the measurements acoustic model transformation matrices may be generated or adapted using maximum likelihood linear regression techniques or other mathematical or statistical approaches. An acoustic model  318  with adapted acoustic model transformation matrices may, for example, be used in subsequent system  100  operation when interference profile records  180  indicating high wind noise (e.g., noise type parameter  306  of wind and noise level parameter  304  of high) are generated. 
     Adaptation  320  (e.g., including supervised learning) may, for example, be performed while vehicle  10  is driven by a driver, and acoustic models  318  may be altered or modified based on the supervised learning. An acoustic model  318  best suited to decoding speech in a signal with high window noise may, for example, have been trained or defined during a supervised learning operation with high wind noise. 
       FIG. 5  is a block diagram of an enhanced spoken dialogue audio prompting system according to embodiments of the present invention. According to some embodiments, interference profile records  180  (e.g., including noise type parameters  306  and noise level parameters  304 ) may be used to modify an audio signal  400  (e.g., output from system  100 ). Interference profile records  180  (e.g., noise type parameters  306  and noise level parameters  304 ) may be used by text to speech  218 , audio processing  220 , or other modules or methods to enhance speech prompting, audio output, or broadcasts output from system  100 . 
     According to some embodiments, modification module  224  may modify parameters associated with audio processing  220  (e.g., prompt level, prompt spectrum, prompt pitch, audio spectrum, audio level, or other parameters) based on an interference profile records  180  (e.g., noise type parameters  306 , noise level parameters  304 , and other parameters). Modification module  224  may, for example, increase prompt level (e.g., volume), alter prompt pitch, shape and/or reshape prompt spectrum (e.g., to increase signal to noise ratio), enhance audio playback (e.g., stereo playback), and/or otherwise enhance or alter audio output from system  100  (e.g., via speaker  40 ). For example, if noise level parameters  304  indicate noise in signal  400  is above a threshold level (e.g., dB level), prompt level (e.g., output from speaker  40 ) audio level  407  may be increased. 
     In some embodiments, a prompt spectrum  402  may, for example, be modified, shaped, or reshaped. A prompt may be an audio or sound output from system  100  including, for example, speech directed to vehicle occupants, and a prompt spectrum  402  may, for example, be an audio spectrum including a range of frequencies, intensities, sound pressures, sound energies, and/or other sound related parameters. Prompt spectrum  402  may, for example, be modified, shaped, or reshaped to increase the signal to noise ratio in vehicle  10  (e.g., in vehicle interior or in proximity of vehicle occupants). Prompt spectrum  402  may, for example, be modified to emphasize or amplify the prompt spectrum  402  in portions of spectrum (e.g., frequency spectrum, energy spectrum, or other type of sound related spectrum) corresponding to high noise energy from vehicle related sounds (e.g., engine noise, wind noise, fan noise, and other sounds). Prompt spectrum  402  may, for example, be amplified in a portion of the spectrum with high noise energy to increase the signal to noise ratio, which may represent the ratio of prompt sound level (e.g. prompt output from system  100 ) to noise level in vehicle interior (e.g., engine noise, wind noise, HVAC fan noise, and other noise). Prompt spectrum  402  may, for example, be modified using audio processor module  220 , text to speech module  218 , or another system or module. 
     In one embodiment, noise type parameters  306  may indicate engine noise (e.g., noise_type parameter=engine) and noise level parameters  304  may represent a level of engine noise. Noise level parameters  304  may, for example, be a quantized representation of engine RPM (e.g., an 8 bit integer or other integer representing engine RPM). Based on noise level parameters  304  (e.g., a quantized representation of engine RPM), modification module  224  may amplify or emphasize predefined portions of prompt spectrum  402 . For example, noise type parameters  306  and noise level parameters  304  may correspond to high noise energy in low frequency portion of a sound spectrum (e.g., below 1000 Hertz (Hz) or another frequency) and low noise energy in high frequency portion of the spectrum (e.g., above 1000 Hertz (Hz) or another frequency). The low frequency portion of prompt frequency spectrum  402  (e.g., below 1000 Hz or another frequency) may be amplified or emphasized to increase the ratio of prompt to engine noise in low frequencies. 
     In some embodiments, audio spectrum  404  (e.g., from stereo, radio or other device) may, for example, be modified or reshaped. Audio spectrum  404  may, for example, be modified or reshaped to increase the audio signal to noise ratio in vehicle  10 . Audio spectrum  404  may, for example, be modified using audio processing module  220  and/or another device or module. Audio spectrum  404  may, for example, be modified to emphasize or amplify the audio spectrum  404  in portions of audio spectrum  404  (e.g., audio frequency spectrum, audio energy spectrum, or other type of sound related spectrum) corresponding to high noise energy from vehicle related sounds (e.g., engine noise, wind noise, fan noise, and other sounds). Audio spectrum  404  may, for example, be amplified in a portion of spectrum with high noise energy to increase the signal to noise ratio, which may represent the ratio of audio (e.g. audio output from speaker  40 ) to noise in vehicle interior. 
     According to some embodiments, audio prompt or audio pitch  406  may be modified or altered based on interference profile records  180 . Prompt or audio pitch  406  may, for example, be modified based on noise type parameters  306  and noise level parameters  304  to increase the clarity and/or intelligibility of a prompt or audio (e.g., output from speakers  40 ). For example, noise type parameters  306  may indicate the presence of wind noise in vehicle  10  and noise level parameters  304  may represent a level of wind noise (e.g., volume of wind noise). Based on noise level parameters  304  (e.g., low, medium, high, or another parameter), the prompt or audio pitch  406  (e.g., related to frequency) may be altered (e.g., made higher or lower). 
     Alteration of prompt or audio pitch  406  may, for example, be dependent upon, proportional to, or otherwise related to noise level parameter  306 . For example, prompt or audio pitch  406  may be altered more in the presence of louder vehicle noises than softer vehicle noises (e.g., may be shifted higher if noise level parameter  304  is high than if noise level parameter  304  is medium or low). In some embodiments, prompt or audio pitch  306  may be decreased or shifted lower based on noise type parameters  306  and noise level parameters  304 . 
     According to some embodiments, modification module  224  may, for example, modify text to speech  218  output by increasing or decreasing speech rate  410 , increasing or decreasing syllable duration  412 , and/or otherwise modifying speech output from system  100  (e.g., via speaker  40 ). Speech rate  410  may, for example, be modified based on noise type parameters  306 , noise level parameters  304 , and/or other information. Speech rate  410  may, for example, be modified to decrease speech rate  410  of a prompt in high noise conditions (e.g., if noise level parameter  306  is high or another value). Decreasing speech rate  410  may, for example, increase intelligibility of spoken dialogue in a loud or high noise environment (e.g., in a vehicle with loud vehicle related sounds). Speech rate  410  may, in some embodiments, be increased based on noise type parameters  306  and noise level parameters  304  to increase intelligibility of a spoken dialogue audio prompt output from system  100 . 
     According to some embodiments, prompt syllable duration  412  may, for example, be modified based on noise type parameters  306 , noise level parameters  304 , and/or other information. Prompt syllable duration  412  may, for example, include the duration of pronunciation of consonants, vowel, and/or other syllables associated with human speech. Syllable duration  412  may, for example, be increased in proportion to, dependent upon, or in relation to noise level parameters  304 . For example, syllable duration  412  may be increased (e.g., duration of syllable pronunciation may be longer) in relation to an increase in vehicle related sounds (e.g., engine noise, HVAC system noise, wind noise and other sounds) represented by noise type parameters  306  and noise level parameters  304 . 
     In some embodiments, a combination of text to speech  218 , audio processing  220 , and/or other types speech prompting or audio output may be modified. Modification module  224  may, for example, use Lombard style or other speech modification. Lombard style modification may model human speech modification or compensation in a loud environment, environment with high background noise, or other high noise level environment. Lombard style modification may, for example, include any combination of signal  400  modification selected from the group including modifying the prompt signal spectrum  402 , modifying the prompt signal pitch  406 , modifying the prompt signal speech rate  410 , and modifying the prompt signal syllable duration  412 . Lombard style modification may, for example, be dependent on noise type parameters  306 , noise level parameters  304 , and other information. For example, noise type parameters  306  of wind (e.g., noise_type=wind) and noise level parameters  304  of high may be generated indicating high wind noise may be present. Based on noise type parameters  306  and noise level parameters  304 , a predefined combination of prompt spectrum  402 , prompt pitch  406 , prompt speech rate  410 , prompt syllable duration  412 , and/or other prompt parameters may be modified to increase intelligibility of the prompt. The predefined combination applied given a combination of noise type parameters  306  and noise level parameters  304  may, for example, be determined during manufacturing, testing, an adaptation  320 , or another process. The predefined combination may, for example, be the combination which best increases the intelligibility, understandability, or clarity of spoken prompt. 
     According to some embodiments, prompt modification may be adapted  320  to improve the clarity and/or intelligibility of prompts. The effectiveness or effect of prompt modification  224  associated with predefined noise type parameters  306 , noise level parameters  304 , and other parameters may be measured and adapted or changed based on the measurement. The effectiveness of prompt modification may, for example, be measured by monitoring user or occupant response to modified prompts. For example, a prompt may be modified based on noise type parameters  306 , noise level parameters  304 , and/or other parameters and occupant response to the prompt may be measured. For example, a prompt may elicit or request a response from an occupant. If the occupant does not respond to prompt, responds to the prompt in an unpredicted manner (e.g., provides a confused response), or performs other actions, it may be determined that prompt modification  224  could be adapted to improve the clarity of prompts. In one example, prompt modification  224  may, for example, be adapted by disabling prompt modification  224 . For example, if it is determined that prompt modification  224  does not improve the clarity or intelligibility of speech prompting, prompt modification  224  (e.g., prompt modification module) may be disabled or deactivated. In one example, prompt modification  224  may be modified by altering prompt modification parameters (e.g., spectrum, pitch, speech rate, syllable duration, and/or other prompt modification parameters). For example, prompt spectrum  402  modification parameters may be adapted or changed to improve the clarity of spoken prompts. Prompt spectrum  402  modification parameters may, for example, be adapted to strengthen or enhance prompt signal  400  in a different part of the prompt spectrum  402 . Other adaptation methods may be used. 
       FIG. 6  is a block diagram of a spoken dialogue control system according to embodiments of the present invention. According to some embodiments, dialogue control  210  or other systems or processes associated with spoken dialogue system  100  may be modified or altered  224  based on noise type parameters  304 , noise level parameters  306 , and/or other parameters. 
     Dialogue control acts  500  may be modified  224  based on interference profile records  180  (e.g., noise type parameters  306 , noise level parameters  304 , and/or other parameters). Dialogue control acts  500  may, for example, be operations performed by dialogue control  210  module and may include prompts output to a user, actions related to determination of input or output, or other operations. Dialogue control acts  500  may for example include clarification acts  502 , reducing semantic interpreter confidence levels  504 , and other processes or operations. Dialogue control acts  500  may, for example, be modified based on interference profile records  180  (e.g., noise type parameters  306 , noise level parameters  304 , and/or other parameters) by implementing clarification acts  502 . Clarification acts  502  may, for example, be implemented or imposed if noise type parameters  306  and noise level parameters  304  indicate high noise may be present in proximity to vehicle  10  (e.g., in vehicle cabin). 
     According to some embodiments, clarification acts  502  may include explicit confirmation of user input, audio prompting or asking a user to repeat input, or otherwise prompting a user to clarify input. An audio prompt  508  requesting explicit confirmation of user input may, for example, be output (e.g., using speaker  40 ). For example, a user may ask (e.g., input speech to spoken dialogue system requesting information) spoken dialogue to find a restaurant (e.g., “Where is the closest restaurant?”). If noise type parameters  306  and noise level parameters  304  indicate high levels or noise (e.g., high levels of vehicle related noise or sounds) are present, spoken dialogue module  210  may, for example, output a prompt requesting confirmation of user&#39;s statement. An audio prompt  508  may, for example, be output asking user to confirm that user is looking for a restaurant (e.g. “did you say ‘where is the closest restaurant?’”). If noise type parameters  306  and noise level parameters  304  indicate background noise may be present, prompts  508  may be output requesting explicit confirmation of user input each time a user provides input, when user input is unintelligible, or at other times. Other clarification acts and prompts may be used. 
     According to some embodiments, clarification acts  502  may include asking or requesting a user to repeat input. Dialogue control module  210  may, for example, output a prompt requesting a user to repeat their input. If, for example, user asks spoken dialogue system  100  to find the closest hotel (e.g., “where is the closest hotel?”) and noise type parameters  306  and/or noise level parameters  304  indicate high noise levels may occur (e.g., noise_level=high), a prompt may be output requesting that user repeat their input. A prompt  508  may, for example, be output asking user to repeat their statement (e.g., “please repeat”, “I didn&#39;t hear that, please say that again”, or other requests for repetition). If noise type parameters  306  and noise level parameters  304  indicate background noise may be present, a prompt  508  may be output requesting user to repeat their input each time user provides input, when user input is unintelligible, or at other times. Other clarification acts  502  may be used. 
     According to some embodiments, clarification acts  502  may be encouraged and/or the likelihood of clarification acts  502  may be increased by altering semantic interpreter confidence levels  504  (e.g., by reducing confidence levels  504  or otherwise altering confidence levels  504 ). Confidence levels  504  may be altered or modified based on noise type parameters  306  and noise level parameters  304 . Confidence levels  504  may, for example, represent the likelihood or certainty that a word string, phrase, or other spoken input (e.g., “find me a hotel”) from a user matches or corresponds to a dialogue act (e.g., inform(type=hotel)) in spoken dialogue system ontology  170 . A confidence level  504  may, for example, be a percentage, numerical value, or other parameter representing a confidence, likelihood, or probability that a word string matches a dialogue act in spoken dialogue system ontology  170 . A confidence level  504  may, for example, be associated with a dialogue act generated by semantic interpreter  206 . Dialogue acts and associated confidence levels  504  may, for example, be output from semantic interpreter  206  to dialogue control module  210 . Dialogue control module  210  may, for example, based on dialogue acts and associated confidence levels  504  generate a response to be output to user. If, for example, confidence level  504  is below a threshold confidence level  506 , dialogue control module  504  may implement clarification acts  502  (e.g., requesting explicit confirmation of user input, requesting user to repeat input, and other clarification acts). If confidence level  504  associated with a dialogue act is above a threshold confidence level  506 , the dialogue act may be deemed to be a correct interpretation of user&#39;s input (e.g., user&#39;s spoken dialogue converted into a word string) and dialogue control module  210  may, for example, generate a response, perform an action, or otherwise respond to the dialogue act. 
     According to some embodiments, confidence levels  504  output from semantic interpreter  206  may, for example, be modified or reduced based on noise type parameters  306 , noise level parameters  304 , and/or other information. For example, if noise level parameters  304  indicate vehicle related noise above a predefined threshold may be present (e.g., noise_level=medium, noise_level=high, or other noise_level value), confidence levels  504  output from semantic interpreter may be reduced. In some embodiments, a confidence level  504  may, for example, be reduced from ninety percent (e.g., 90%) to, for example, eighty percent (e.g., 80%) or another value if noise type parameters  306  and/or noise level parameters  304  indicate moderate to high noise levels may occur in vehicle  10  (e.g., in vehicle passenger compartment). Other confidence levels  504  may be used. 
     Reduction in confidence levels  504  may, for example, be non-linear. Confidence levels  504  above a predefined boundary confidence level may, for example, not be reduced or altered regardless of whether noise type parameters  306  and/or noise level parameters  304  indicate background noise may be present. For example, confidence levels  504  (e.g., associated with dialogue acts) above a boundary threshold (e.g., ninety-five percent or another value) may not be altered or reduced while confidence levels  504  below a boundary threshold (e.g., ninety-five percent or another value) may be reduced. Other boundary thresholds may be used. 
     According to some embodiments, modification of dialogue control acts  500  given interference profile records (e.g., noise type parameters  306 , noise level parameters  304 , and other information) may be adapted  320 . Modification  224  of dialogue control acts  500  (e.g., implementing clarification acts  502 , reducing confidence levels  504 , and other modifications) may, for example, be adapted by measuring correlations between noise type parameters  306  and/or noise level parameters  304  and dialogue control  210  success or functionality. An optimal modification of dialogue control  210  for a given interference profile record  180  may, for example, be determined in an adaptation process  320 . An optimal modification of dialogue control for a given interference profile record  180  may be the modification which is least cumbersome to a user and/or best improves system  100  functionality. For example, noise type parameters  306  and noise level parameters  304  may indicate that high wind noise may be present and semantic interpreter confidence levels  504  may be modified  224  based on the noise type parameters  306  and noise level parameters  304 . Dialogue control  210  function (e.g., success of dialogue control  210  or dialogue control  210  success) with modified confidence levels  504  may be measured. Dialogue control  210  function or success may, for example, be measured based on whether dialogue control  210  outputs an appropriate response to user input. For example, if user inputs a request for the location of the closest gas station (e.g., “where is the closest gas station?”), a dialogue control  210  response listing gas stations would be deemed a dialogue success while an off topic audio prompt  508  (e.g., “the closest restaurants are restaurant A and restaurant B”) output from dialogue control  210  would not be considered a success. Other success measurement approaches may be used. Based on the measurement of dialogue control  210  function or success, dialogue control acts  500  given interference profile records  180  may adapted to improve the function of dialogue control  210  system. For example, adaptation  320  may determine that clarification acts  502  (e.g., explicit confirmation of user input, asking for user to repeat input) are more effective than reducing semantic interpreter confidence levels  504  when noise type parameters  306  and noise level parameters  304  indicate high wind noise may be present. For example, adaptation  320  may determine that reducing confidence levels  504  (e.g., by a predetermined confidence level reduction parameter or amount) is the most effective and least cumbersome for the user when noise type parameters  306  and noise level parameters  304  indicate high engine noise may be present. Modification  224  of dialogue control acts  500  (e.g., implementing clarification acts  502 , reducing confidence levels  504 , and other modifications) may, for example, be adapted to use the most effective and least cumbersome dialogue control acts  500  given a set of noise type parameters  306  and noise level parameters  304 . 
     According to some embodiments, audio prompts  508  may be introduced and/or modified based on interference profile records  180  (e.g., noise type parameters  306 , noise level parameters  304 , and other information). Prompts  508  may, for example, include information output from system  100  and may be generated by dialogue control module  210  in response to user input. Prompts  508  may typically be output from system  100  in response to user input, to provide information to user, or for other functions. Prompts  508  may, in some embodiments, inform a user that spoken dialogue system  100  functions and/or performance may be reduced or changed due to high background noise. Prompts  508  may, for example, be generated based on noise type parameters  306  and/or noise level parameters  304 . Prompts  508  may, for example, set a user&#39;s expectation of spoken dialogue system  100  performance (e.g., that system  100  performance may be reduced), prepare a user for different interaction style (e.g., inform user that system  100  may request user to clarify statements, repeat statements, and perform other functions), or otherwise inform a user that system  100  performance may be altered in the presence of background noise. Noise type parameters  306  and noise level parameters  304  may, for example, indicate high wind noise. Based on the noise type parameters  306  and noise level parameters  304  indicating high wind noise, a prompt  508  may be generated by dialogue control module  210  and output to user (e.g., using speakers  40 ). Prompt  508  may, for example, set user expectations of system  100  performance with high wind noise. Prompt  508  may, for example, be “please note that voice recognition with windows open at high speed is difficult” or another prompt  508 . Based on prompt  508 , user may consider closing vehicle window(s) to improve system  100  performance. In some embodiments, prompt  508  may based on noise type parameters  306  and noise level parameters  304  prepare a user for a different spoken dialogue interaction style. Prompt  508  may, for example, be “voice recognition is difficult, I may ask for more clarifications, bear with me, where would you like to go?” or another prompt. Based on prompt  508 , user&#39;s expectations may be managed and user may, for example, be prepared or pre-warned that system  100  may output more clarification acts  502  (e.g., requests for clarification, repeat, and other clarifications) and/or system  100  functions may be modified (e.g., to compensate for high levels of background noise). 
     According to some embodiments, the pace and/or timing of prompts  508  may be modified or controlled based on interference profile records  180  (e.g., noise type parameters  306 , noise level parameters  304 , and other information). The timing of prompt  508  output may, for example, be modified or delayed to output prompt  508  to user at a time when lower background noise (e.g., vehicle related sounds) may be present in vehicle  10 . For example, noise type parameters  306  and noise level parameters  304  may indicate high engine noise may be present in vehicle (e.g., noise_type=engine and noise_level=high). Noise type parameters  306  and noise level parameters  304  of high engine noise may, for example, indicate that engine RPM may be high (e.g., driver may be accelerating vehicle  10 ). Based on noise type parameters  306  and noise level parameters  304  indicating high engine noise, dialogue control  210  may delay prompt  508  output. Dialogue control  210  may, for example, delay a prompt  508  output until noise level parameters  304  indicate engine noise may be reduced. Dialogue control  210  may, in some embodiments, delay a prompt  508  output for a predetermined period of time. The predetermined period of time may, for example, be a typical or average amount of time for vehicle acceleration, may be based on typical driver characteristics (e.g., typical acceleration times), or may be another time period. A typical or average acceleration time may, for example, be determined during vehicle testing, manufacturing, or during a spoken dialogue adaptation process  320 . 
     According to some embodiments, dialogue style  514  may be modified to alter or reduce grammar perplexity  510  or based on interference profile records  180  (e.g., noise type parameters  306 , noise level parameters  304 , and/or other information). Grammar perplexity  510  may, for example, be the complexity of speech recognition grammar used by speech recognition module or device  204  at a given time. Dialogue control module  210  may, for example, determine grammar perplexity based on interference profile records  180 . Grammar perplexity  510  may, for example, be reduced or modified by performing single slot recognition, enforcing exact phrasing, avoiding mixed initiative, and/or using other techniques or approaches. Grammar perplexity  510  may, for example, be reduced or altered based on noise type parameters  306  and noise level parameters  304 . For example, noise type parameters  306  and noise level parameters  304  may indicate that high wind noise (e.g., noise_type=wind, noise_level=high) may be present. Based on noise type parameters  306  and noise level parameters  304  indicating high wind noise, dialogue control  210  may reduce grammar perplexity  510  by performing single slot recognition, enforcing exact phrasing, avoiding mixed initiative, and/or performing other actions. 
     Single slot recognition may, for example, reduce grammar perplexity  510  by reducing or modifying complex prompts requesting multiple slots or types of information into multiple simpler audio prompts requesting a reduced number of or single slots of information. For example, a complex prompt of “what music would you like to hear?” may be modified or reduced to multiple single slot prompts of “please enter song title” followed by “please enter the artist” and/or other prompts. Other prompts related to other topics may of course be used. 
     In some embodiments, dialogue style  514  may be modified to reduce grammar perplexity  510  by enforcing exact phrasing from a user (e.g., vehicle occupant(s)). Exact phrasing from a user may be enforced by prompting a user to provide exact responses rather than general responses. For example, a prompt  508  of “Which service would like?”, which may elicit many different responses from a user may be modified to be prompt  508  of “please say one of a. music, b. directions, c. climate control”, which may elicit specific or exact phrasing from a user. If noise type parameters  306  and/or noise level parameters  304  indicate high levels of noise (e.g., wind, engine, HVAC system, audio playback or other noise) may be present in vehicle, dialogue control module  210  may enforce exact phrasing from a user. Other prompts related to other topics may of course be used. 
     In some embodiments, dialogue style  514  may be modified to reduce grammar perplexity  510  by reducing mixed initiative dialogue style  514 . Mixed initiative dialogue style  514  may, for example, allow a user to respond to a question which they were not asked. Mixed initiative may, for example, be disabled or deactivated to reduce grammar perplexity  510  if noise type parameters  306  and/or noise level parameters  304  indicate noise levels above a threshold may be present. For example, dialogue control  210  may output a prompt requesting a type of information (e.g., “what type of hotel are you looking for?”), and mixed initiative may allow a user to provide an off topic response (e.g., “where is the closest restaurant?”). Other prompts  508  related to other topics may be used. Disabling mixed initiative may, for example, require a user to respond to a question asked not allowing user to change conversation topic. If a user provides an off topic response to a question, dialogue control module  210  may request that user response respond to the question asked. 
     According to some embodiments, modification of dialogue style  514  given interference profile records  180  (e.g., noise type parameters  306 , noise level parameters  304 , and other parameters or information) may be adapted  320 . Modification  224  of dialogue style  514  (e.g., altering grammar perplexity  510  or other dialogue style modifications) may, for example, be adapted by measuring correlations between modification of dialogue style  514  based on interference profile records  180  (e.g., noise type parameters  306  and/or noise level parameters  304 ) and dialogue control  210  success or functionality. An optimal modification of dialogue style  514  or grammar perplexity  510  reduction approach (e.g., single slot recognition, enforcing exact phrasing, avoiding mixed initiative, or other grammar perplexity reduction approach) for a given interference profile record  180  may be determined. The optimal modification of dialogue style  514  for a given interference profile record  180  may be the modification which is least cumbersome to a user, most improves system  100  functionality, and/or results in dialogue success. An optimal modification of dialogue style  514  may, for example, be determined by measuring dialogue control  210  success with and without modification of dialogue style  514  or grammar perplexity  510 . Measured dialogue control success associated with different types of modification of dialogue style  514  or grammar perplexity  510  may be compared to determine a modification of dialogue style  514  or grammar perplexity  510 , which most improves dialogue control success. For example, interference profile records  180  (e.g., noise type parameters  306  and noise level parameters  304 ) may indicate that high HVAC related noise may be present and grammar perplexity  510  may be reduced or modified  224  based on the interference profile records  180 . Grammar perplexity  510  may, for example, be reduced by modifying dialogue style  514  to enforce exact phrasing (e.g., prompting a user to choose from a list of options (e.g., “Please say one of a. music, b. directions, or c. gas” instead of “which service would you like?’)). Dialogue control  210  success (e.g., success of dialogue control system  210 ) with enforcement of exact phrasing (e.g., reduced grammar perplexity  510 ) may be measured. Dialogue control  210  function or success may, for example, be measured based on whether a user completes a dialogue action (e.g., responding to a prompt) correctly, whether user achieves a positive dialogue result (e.g., user finds what they are looking for), or based on other metrics or parameters. Dialogue control  210  success (e.g., success of dialogue control system  210 ) with enforcement of exact phrasing (e.g., reduced grammar perplexity  510 ) may be compared to dialogue control  210  success without exact phrasing or dialogue control success  210  with another type of modification of dialogue style  514  or grammar perplexity  510 . For example, it may be determined that a type of dialogue style  514  modification to reduce grammar perplexity  510  (e.g., single slot recognition) based on certain interference profile records  180  (e.g., noise type parameters  306  and noise level parameters  304 ) may result in reduced dialogue control success or be less successful than another type of dialogue style  514  modification and/or no modification to reduce grammar perplexity  510 . Based on the determination that a type of dialogue style  514  modification given certain interference profile records  180  may be less successful or unsuccessful in increasing dialogue success, the type of dialogue style  514  modification may, for example, be disabled, adapted, and/or replaced by a different type of dialogue style  514  modification. For example, adaptation  320  may determine that reducing grammar perplexity  510  by enforcing exact phrasing may be more effective than avoiding mixed initiative when noise type parameters  306  and noise level parameters  304  indicate high HVAC noise or other vehicle relate noise may be present. For example, adaptation  320  may determine that reducing grammar perplexity  510  by enforcing exact phrasing may be the most effective and least cumbersome for the user when noise type parameters  306  and noise level parameters  304  indicate high HVAC noise may be present. 
     According to some embodiments, dialogue control  210  may, based on interference profile records  180  (e.g., noise level parameters  304 , noise type parameters  306 , and other information), monitor (e.g., listen for) and respond to user confusion  516 . If noise type parameters  306  and noise level parameters  304  indicate high noise levels may be present in or around vehicle  10 , dialogue control  210  may, for example, be modified to monitor or listen for and respond to user confusion  516 . In order to monitor and respond to user confusion  516 , dialogue control  210  may, for example, be modified to identify clarification requests input from user. Clarification requests (e.g., spoken by a user) may, for example, include phrases such as “repeat,” “I can&#39;t hear you,” “repeat this prompt,” “it&#39;s not clear,” “what&#39;s that?”, or other phrases. Clarification requests from a user may, for example, be responded to by dialogue control  210 . Dialog control  210  may, for example, respond to clarification requests from a user by repeating the last prompt output, rephrasing the last prompt, or performing other actions. A prompt  508  (e.g., “the closest restaurant is ABC diner” or another prompt) may, for example, be rephrased by changing the order of phrases in prompt  508  (e.g., “ABC is the nearest restaurant”). Other prompts may be used. 
     According to some embodiments, multi-modal, multi-function, or other type of dialogue may be modified based on interference profile records  180  (e.g., noise type parameters  306 , noise level parameters  304 , and/or other information). Multi-modal dialogue  512  may, for example, include spoken dialogue combined with tactile, visual, or other dialogue. Multi-modal dialogue  512  may, for example, include spoken dialogue audio prompts requesting user to input information into a tactile device (e.g., input device  44  or another device). Other types of multi-modal dialogue  512  may be used. 
     In some embodiments, if noise type parameters  306  and noise level parameters  304  indicate high levels of noise may be present in or around vehicle  10 , multi-modal dialogue  512  may, for example, be modified by reverting to or favoring visual display over speech prompting, by reverting to or switching to visual display of system hypotheses (e.g., questions, requests for information, and other prompts), prompting or requesting tactile confirmation from a user (e.g., select response from list of responses displayed on touchscreen or other output device), encouraging use of tactile modality (e.g., reduce confidence of the semantic interpreter), switching from speech to other modalities for a subset of application functions (e.g., simple command and control by tactile means), or other modifications. 
     Based on noise type parameters  306  and noise level parameters  304 , dialogue control module  210  may, for example, revert to visual display of system hypotheses by displaying questions, requests for information, and other types of prompts on an output device  42  (e.g., a display screen). Tactile confirmation may, for example, be requested from a user. Dialogue control  210  may, for example, request that user confirm responses to dialogue prompts  508  (e.g., spoken dialogue prompts) or other information output from system  100  using a tactile device, input device  44  (e.g., keyboard, touchscreen, or other input device), and/or other device. System  100  may, for example, output a statement “please confirm that you said hotel by entering yes” using speaker  40 , output device  42 , or other device, and user may provide tactile confirmation by entering a response (e.g., pressing a button, entering “yes” or other response) into an input device  44  or other device. Dialogue control module  210  may, in some embodiments, request that a user select a response from a list of options. For example, system  100  may prompt user to select an option from a list of options using a tactile device, input device  44  (e.g., keyboard, touchscreen, or other input device), and/or other device. System  100  may, for example, output a prompt “please choose a category: hotels, restaurants, or gas stations on touchscreen” and user may respond to the prompt by entering choosing an option (e.g., hotels, restaurants, or gas stations) on a tactile device, input device  44 , and/or other device. 
     According to some embodiments, modification module  224  may, for example, encourage or increase use of tactile dialogue by altering semantic interpreter confidence levels  504 . If, for example, a confidence level  504  is below a threshold confidence level  506 , dialogue control module  504  may request tactile confirmation, tactile selection, or other type of input from user. If confidence level  504  associated with a dialogue act is above a threshold confidence level  506 , the dialogue act may be deemed to be a correct interpretation of user&#39;s input, and system  100  may use speech based dialogue control (e.g., system  100  may not request tactile confirmation, tactile selection, or other type of input from user). Confidence levels  504  may, for example, be reduced based on interference profile records  180  (e.g., noise type parameters  306 , noise level parameters  304 , or other information). For example, if interference profile records  180  (e.g., noise level parameter  304 ) indicates vehicle noise related noise above a predefined threshold may be present (e.g., noise_level=medium, noise_level=high, or other noise_level value), confidence levels  504  output from semantic interpreter may be reduced. A confidence level  504  may, for example, be a continuous value (e.g., between 0% and 100% or another range of values) related to or depending on a certainty in speech recognition. Confidence levels  504  may, for example, be altered (e.g., reduced or increased) from a first confidence level value to a second confidence level value (e.g., a confidence level value less than a first confidence level value) based on interference profile records  180 . Confidence levels  504  may, for example, be altered (e.g., reduced or increased) according to function (e.g., a continuous function). A confidence level  504  may, for example, be ninety-five percent (e.g., 95%) or any other value if noise level parameter  304  indicates zero or low background noise (e.g., noise level parameter=low). A confidence level  504  may, for example, be reduced from a first value (e.g., ninety-five percent or another value) to, for example, a second value (e.g., eighty percent or another value), which may, for example, be less than a first value if interference profile records  180  indicate moderate to high noise levels may occur in vehicle  10  (e.g., in vehicle passenger compartment). Reducing confidence levels  504  if interference profile records  180  (e.g., noise type parameters  306  and/or noise level parameters  304 ) indicate high background noise may increase likelihood that dialogue control  210  may request tactile confirmation, selection or other tactile input from user. 
     According to some embodiments, multi-modal dialogue may be modified  224  by switching from speech to other modalities (e.g., tactile input, visual output, and/or other modalities) for a subset of system  100  functions (e.g., predefined back-end application  212  functions). Based on noise type parameters  306 , noise level parameters  304 , and/or other information, one or more back-end applications  212  may be switched from speech based modality to non-speech speech modalities (e.g., tactile or other modalities). Other back-end applications  212  may, for example, not be switched to non-speech modalities (e.g., control and/or command may remain speech based). For example, if noise type parameters  306  and noise level parameters  304  indicate high engine noise (e.g., noise_type=engine, noise_level=high), predefined back-end application  212  (e.g., radio, map, voice search, or other back-end application) functionality (e.g., control and command) may be switched from speech based to tactile based control (e.g., using input device  44 ) while other back-end applications  212  may not be switched from speech to tactile based control. For example, if sound type parameters  306  and/or sound level parameters  304  indicate background noise, voice search and/or other background application(s)  212  may be disabled (e.g., locked out), and speech based radio control and/or other background applications  212  may not be disabled (e.g., may remain active). Which back-end applications  212  are switched to other modalities (e.g., tactile input or other mode of input) or deactivated if sound type parameters  306  and/or sound level parameters  304  indicate background noise may, for example, be determined during vehicle testing, manufacturing, or during adaptation  320 . 
     According to some embodiments, modification of multi-modal dialogue  512  given interference profile records  180  (e.g., noise type parameters  306 , noise level parameters  304 , and other information) may be adapted  320 . Modification  224  of multi-modal dialogue  512  (e.g., reverting to visual display, requesting tactile confirmation, encouraging use of tactile modalities, switching from speech to other modalities for a subset of application functions and/or other modifications) may, for example, be adapted  320  by measuring correlations between noise type parameters  306  and/or noise level parameters  304  and dialogue control  210  success or functionality. Adaptation  320  may, for example, determine the optimal modification of multi-modal dialogue  512  (e.g., reverting to visual display, requesting tactile confirmation, encouraging use of tactile modalities, switching from speech to other modalities for a subset of application functions and/or other modifications) for a given interference profile record  180 . The optimal modification of dialogue style  514  for a given interference profile record  180  may be the modification which is least cumbersome to a user and/or best improves system  100  functionality. Adaptation  320  of multi-modal dialogue  512  modification policies or approaches may be similar to adaptation of dialogue style  514  modification policies, adaptation of dialogue control acts  500 , and other adaptation  320  processes or approaches. 
     In some embodiments, all types of modification  224  of dialogue control  210  operations based on noise type profiles  306  and noise level profiles  304  may be adapted  320 . Types of modification  224 , as discussed herein, may include modification of dialogue control acts  500 , introduction of audio prompts  508 , modification of prompts  508 , modification of dialogue style  514  (e.g., to reduce grammar perplexity  510 ), monitoring and responding to user confusion  516 , modification of multi-modal dialogue  512 , modification of back-end application  212  functions, and/or other types of modification  224 . The correlation between dialogue success and modification of dialogue control  210  based on noise type parameters  306  and/or noise level parameters  304  may be measured, evaluated, or calculated. The success of a type of dialogue control  210  modification  224  may, for example, be measured or evaluated by determining whether a user provides predictable responses to dialogue control prompts  508  (e.g., whether user responses are on or off topic), whether user provides any response to prompts  508 , or using other approaches. Based on the measured dialogue control success, modification of dialogue control  210  processes and operations may be adapted by deactivating, disabling, altering or switching types of dialogue control modification  224 , or otherwise altering dialogue control modification  224 . Dialogue control modification  224  operations may be altered by, for example, changing the parameters associated with a type modification  210  given noise type parameters  306  and noise level parameters  304 . For example, semantic interpreter confidence levels  504  may be altered, parameters related to pace and timing of prompts  508  may be altered, and other parameters may be altered or adapted to improve dialogue control  210  success. Other parameters and operations may be adapted or changed. 
       FIG. 7  is a flow chart of a method according to embodiments of the present invention. In operation  600 , sound related vehicle information (e.g., sound related vehicle information  160  of  FIG. 2 , or signals or information related to the operation of vehicle systems producing or causing sound) representing or corresponding to one or more sounds may be received in a processor (e.g., interference profiling module  222  of  FIG. 3 ) associated with a vehicle (e.g., vehicle  10  of  FIG. 2 ). The sound related vehicle information may in some embodiments not include an audio signal. Interference profiling module  222  may, for example, be implemented all or in part by processor  110 . 
     In operation  610 , interference profile records (e.g., interference profile records  180  of  FIG. 2 ) may be determined based on the sound related vehicle information. The interference profile records may, for example, be noise type parameters (e.g., noise type parameters  306  of  FIG. 5 ), noise level parameters (e.g., noise level parameters  304  of  FIG. 5 ), and/or other parameters. The interference profile records may, for example, be determined using a logical operation or other mathematical operations based on multiple types of sound related vehicle information. The interference profile records may, in some embodiments, be determined by quantizing sound related vehicle information (e.g., vehicle engine RPM information). 
     In operation  620 , an audio signal (e.g., prompt signal  400  of  FIG. 5 ) output to a passenger may be modified based on the sound related vehicle information and/or the interference profile records. An audio signal output to a passenger may, for example, be modified by shaping or reshaping prompt signal spectrum (e.g., prompt signal spectrum  402  of  FIG. 5 ), modifying prompt signal pitch (e.g., prompt signal pitch  406  of  FIG. 5 ), modifying prompt signal speech rate (e.g., prompt signal speech rate  410 ), modifying prompt signal syllable duration (e.g., prompt signal syllable duration  412 ), or using other audio and/or prompt signal modification approaches. 
     Other or different series of operations may be used. 
     Embodiments of the present invention may include apparatuses for performing the operations described herein. Such apparatuses may be specially constructed for the desired purposes, or may comprise computers or processors selectively activated or reconfigured by a computer program stored in the computers. Such computer programs may be stored in a computer-readable or processor-readable non-transitory storage medium, any type of disk including floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs) electrically programmable read-only memories (EPROMs), electrically erasable and programmable read only memories (EEPROMs), magnetic or optical cards, or any other type of media suitable for storing electronic instructions. It will be appreciated that a variety of programming languages may be used to implement the teachings of the invention as described herein. Embodiments of the invention may include an article such as a non-transitory computer or processor readable non-transitory storage medium, such as for example a memory, a disk drive, or a USB flash memory encoding, including or storing instructions, e.g., computer-executable instructions, which when executed by a processor or controller, cause the processor or controller to carry out methods disclosed herein. The instructions may cause the processor or controller to execute processes that carry out methods disclosed herein. 
     Different embodiments are disclosed herein. Features of certain embodiments may be combined with features of other embodiments; thus certain embodiments may be combinations of features of multiple embodiments. The foregoing description of the embodiments of the invention has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed. It should be appreciated by persons skilled in the art that many modifications, variations, substitutions, changes, and equivalents are possible in light of the above teaching. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.