Abstract:
A method for digitally generating speech with improved prosodic characteristics can include receiving a speech input, determining at least one prosodic characteristic contained within the speech input, and generating a speech output including the prosodic characteristic within the speech output. Consequently, the method can adjust speech output based on prosodic features within the speech input.

Description:
BACKGROUND OF THE INVENTION  
         [0001]    1. Technical Field  
           [0002]    The present invention relates to the field of synthetic speech generation.  
           [0003]    2. Description of the Related Art  
           [0004]    Synthetic speech generation is used in a multitude of situations; a few of which include: interactive voice response (IVR) applications, devices to aid specific handicaps, such as blindness, embedded computing systems, such as vehicle navigation systems, educational systems for automated teaching, and children&#39;s electronic toys. In many of these situations, such as IVR applications, customer acceptance and satisfaction of a system is critical.  
           [0005]    For example, IVR applications can be designed for customer convenience and to reduce business operating costs by reducing telephone related staffing requirements. In the event that customers are dissatisfied with the IVR system, individual customers will either opt out of the IVR system to speak with a human agent, will become generally disgruntled and factor their dissatisfaction into future purchasing decisions, or simply refuse to utilize the IVR system at all.  
           [0006]    One reason many users dislike systems that provide synthetically generated speech is that such speech can sound mechanical or unnatural and can be audibly unpleasant, even difficult to comprehend. Unnatural vocal distortions can be especially prominent when the speech generated relates to proper nouns, such as people, places, and things due to the many exceptions to rules of pronunciation that can exist for these types of words. Prosodic flaws in the synthetically generated speech can cause the speech to sound unnatural.  
           [0007]    Prosodic characteristics relate to the rhythmic aspects of language or the suprasegmental phonemes of pitch, stress, rhythm, juncture, nasalization, and voicing. Speech segments can include many discernable prosodic characteristics, such as audible changes in pitch, loudness, and syllable length. Synthetically generated speech can sound unnatural to listeners due to prosodic flaws within the synthetically generated speech, such as the speed, the loudness in context, and the pitch of the generated speech.  
         SUMMARY OF THE INVENTION  
         [0008]    The invention disclosed herein provides a method and a system for generating synthetic speech with prosodic responses with improved prosodic characteristics over conventional synthetic speech. In particular, a speech generation system can extract prosodic characteristics from speech inputs provided by system users. The extracted prosodic characteristics can be applied to synthetically generated speech responses. Prosodic characteristics that can be extracted and applied can include, but are not limited to, the speed before and after each word, the pauses occurring before and after each word, the rhythm of utilized words, the relative tones of each word, and the relative stresses of each word, syllable, or syllable combination. By applying extracted prosodic characteristics, speech generation systems can create synthetic speech that sounds more natural to the user, thereby increasing the understandability of the speech and providing a better overall user experience.  
           [0009]    One aspect of the present invention can include a method for synthetically generating speech with improved prosodic characteristics. The method can include receiving a speech input, determining at least one prosodic characteristic contained within the speech input, generating a speech output including the extracted prosodic characteristic. The at least one prosodic characteristic can be selected from the group consisting of the speed before and after a word, the pause before and after a word, the rhyme of words, the relative tones of a word, and the relative stresses applied to a word, a syllable, or a syllable combination. In one embodiment, the receiving step and the generating step can be performed by an interactive voice response system. In another embodiment, the receiving step can occur during a first session and the generating step can occur during a second session. The first session and the second session can represent two different interactive periods for a common user. Upon completing the determining step, the prosodic characteristic can be stored in a data store, and before the generating step, the prosodic characteristic can be retrieved from the data store.  
           [0010]    In one embodiment, the speech input can be converted into an input text string and a function can be performed responsive to information contained within the input text string. In a further embodiment, an output text string can be generated responsive to the performed function. The output text string can be converted into the speech output. In another embodiment, the speech output can include a portion of the speech input, wherein this portion of the speech output utilizes the prosodic characteristic of the speech input. In yet another embodiment, a part of speech can be identified, where the part of speech is associated with at least one word within the speech input. The prosodic characteristic can be detected for at least one selected part of speech. This part of speech can be a proper noun.  
           [0011]    Another aspect of the present invention can include a system for generating synthetic speech including a speech recognition component capable of extracting prosodic characteristics from speech input. A text-to-speech component capable of modifying at least a portion of synthetically generated speech based upon at least a portion of the prosodic characteristics can also be included. Moreover, a prosodic characteristic store configured to store and permit retrieval of the prosodic characteristics can be included. In one embodiment, the system can be an interactive voice response system.  
           [0012]    Another aspect of the present invention can include a system for synthetically generating speech including receiving a speech input, analyzing the speech input to generate special handling instructions, and altering at least one speech generation characteristic of a text-to-speech application based upon the special handling instructions. The special handling instructions can alter output based upon a language proficiency level and/or an emotional state of the listener. The speech generation characteristic can alter the clarity and/or pace of speech output.  
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0013]    There are shown in the drawings embodiments, which are presently preferred, it being understood, however, that the invention is not limited to the precise arrangements and instrumentalities shown.  
         [0014]    [0014]FIG. 1 is a schematic diagram illustrating a speech generation system that can extract and utilize prosodic characteristics from speech inputs in accordance with the inventive arrangements disclosed herein.  
         [0015]    [0015]FIG. 2 is a flow chart illustrating a method for extracting and subsequently applying prosodic characteristics using the system of FIG. 1.  
     
    
     DETAILED DESCRIPTION OF THE INVENTION  
       [0016]    The invention disclosed herein provides a method and a system for digitally generating speech responses with improved prosodic characteristics. More particularly, the invention can extract prosodic characteristics from a speech input during a speech recognition process. These prosodic characteristics can be applied when generating a subsequent speech output. Particular prosodic characteristics that can be extracted and later applied can include, but are not limited to, the speed before and after each word, the pauses occurring before and after each word, the rhythm of utilized words, the relative tones of each word, and the relative stresses of each word, syllable, or syllable combination.  
         [0017]    [0017]FIG. 1 is a schematic diagram illustrating a system  100  that can extract and later apply prosodic characteristics. The system  100  can include a speech recognition application  105 , a text-to-speech application  140 , a prosodic characteristic store  135 , and a back-end system  130 .  
         [0018]    The speech recognition application  105  can convert a verbal speech input  108  into a representative textual string  128 . During this conversion process, the speech recognition application  105  can extract prosodic characteristics  127  from the speech input  108 . For example, the speech recognition application  105  can detect and extract durational information pertinent to the pauses that occur before and after words within the speech input  108 . Any of a variety of approaches can be utilized by the speech recognition application  105  to perform its functions so long as the selected approach allows prosodic characteristics  127  to be extracted from the speech input  108 .  
         [0019]    The text-to-speech application  140  can allow the system  100  to convert a textual output to a speech output that can be transmitted to a user. The text-to-speech speech application  140  can perform text-to-speech conversions in many different manners. For example, the text-to-speech application  140  can utilize a rule based approach where individual phoneme segments can be joined through computer based rules specifying phoneme behavior within the context of the generated speech output  158 . Alternately, the text-to-speech application  140  can utilize a concatenative synthesis approach where stored intervals of natural speech are joined together, stretched/compressed, and otherwise altered to satisfy the requirements set by the preceding acoustic-prosodic components. Any approach where stored prosodic characteristics  127  can be incorporated into generated speech can be utilized by the text-to-speech application  140 . As used herein, a phoneme can be the smallest phonetic unit in a language that is capable of conveying a distinction in meaning, such as the “m” of “mat” and the “b” of “bat” in the English language.  
         [0020]    The prosodic characteristic store  135  can store prosodic characteristics  127 , such as those received from the speech recognition application  105 , for later retrieval by the text-to-speech application  140 . The prosodic characteristic store  135  can utilize a temporary storage location such as random access memory (RAM) to store prosodic characteristics  127  in allocated variable locations. In an alternate example, the prosodic characteristic store  135  can utilize a more permanent storage location, such as a local or networked hard drive or a recordable compact disk (CD), to store the prosodic characteristics  127  for longer time periods.  
         [0021]    The back-end system  130  can be system that utilizes a speech recognition application  105  and a text-to-speech application  140  within its operation. For example, the back-end system  130  can be an integrated voice response (IVR) system that accepts speech input  108  from a caller, converts the input to a text string  128 , performs an action or series of actions resulting in the generation of a text string  142 , which is converted to a speech output  158 , and transmitted to the caller. In another embodiment, the back-end system  130  can be a software dictation system, wherein a user&#39;s verbal input is converted into text. In such an embodiment, the dictation system can generate speech queries for clarification, wherever the dictation system is uncertain of a portion of the speech input and thereby is unable to generate a transcription.  
         [0022]    In operation, a speech input  108 , such as a vocal response for an account number, can be received by the speech recognition application  105 . A pre-filtering component  110  can be used to remove background noise from the input signal. For example, static from a poor cellular connection or background environmental noise can be filtered by the pre-filtering component  110 . A feature detection component  115  can segment the input signal into identifiable phonetic sequences. Multiple possible sequences for each segment can be identified by the feature detection component  115  as possible phonemes for a given input signal.  
         [0023]    Once an input has been separated into potential phonetic sequences, the unit matching component  120  can be utilized to select among alternative phonemes. The unit matching component  120  can utilize speaker-independent features as well as speaker dependant ones. For example, the speech recognition application  105  can adapt speaker-independent acoustic models to those of the current speaker according to stored training data. This training data can be disposed within a data store of previously recognized words spoken by a particular speaker that the speech recognition application  105  has “learned” to properly recognize. In one embodiment, the speaker-independent acoustic models for a unit matching component  120  can account for different languages, dialects, and accents by categorizing a speaker according to detected vocal characteristics. The syntactic analysis  125  can further refine the input signal by contextually examining individual phoneme segments and words. This contextual analysis can account for many pronunciation idiosyncrasies, such as homonyms and silent letters. The results of the syntactic analysis  125  can be an input text string  128  that can be interpreted by the back-end system  130 .  
         [0024]    The back-end system  130  can responsively generate an output text string  142  that is to be ultimately converted into a speech output  158 . A linguistic analysis component  145  can translate the output text string  142  from one string of symbols (e.g. orthographic characters) into another string of symbols (e.g. an annotated linguistic analysis set) using a finite state transducer, which can be an abstract machine containing a finite number of states that is capable of such symbol translations. One purpose of the linguistic analysis component  145  is to determine the grammatical structure of the output text string  142  and annotate the text string appropriately. For example, since different types of phrases, such as interrogatory verse declarative phrases, can have different stresses, pitches, and intonation qualities, the linguistic analysis component can detect and account for these differences.  
         [0025]    Annotating the output text string  142  within the linguistic analysis  145  component in a manner cognizable by the prosody component  150  allows the text-to-speech application  140  to perform text-to-speech conversions in a modular fashion. Such a modular approach can be useful when constructing flexible, language independent text-to-speech applications. In language independent applications, different linguistic descriptions can be utilized within the linguistic analysis component  145 , where each description can correspond to a particular language.  
         [0026]    The prosody component  150  can receive an annotated linguistic analysis set (representing a linguistically analyzed text segment) from the linguistic analysis component  145  and incorporate annotations into the string for prosodic characteristics. In annotating the received text segment, the prosodic component  145  can segment received input into smaller phonetic segments. Each of these phonetic segments can be assigned a segment identity, a duration, context information, accent information, and syllable stress values.  
         [0027]    The prosody component  150  can also annotate information on how individual phonetic segments are to be joined to one another. The joining of phonetic segments can form the intonation for the speech to be generated that can be described within a fundamental frequency contour (F 0 ). Since human listeners can be sensitive to small changes in alignment of pitch peaks with syllables, this fundamental frequency contour can be very important in generating natural sounding speech.  
         [0028]    In one embodiment, the fundamental frequency contour can be generated using time-dependent curves. Such curves can include a phrase curve (which can depend on the type of phrase, e.g., declarative vs. interrogative), accent curves (where each accented syllable followed by non-accented syllables can form a distinct accent curve), and perturbation curves (that can account for various obstruents that occur in human speech). Other embodiments can generate the fundamental frequency contour using the aforementioned curves individually, in combination with one another, and/or in combination with other intonation algorithms.  
         [0029]    The prosody component  150  can utilize data from the prosodic characteristic store  135  including both durational and intonation information. For example, if the prosodic characteristic extractor  130  detected and recorded information about the rhythm of words used within the speech input  108 , the fundamental frequency contour for the generated text can be modified to more closely coincide with the previously detected rhythm of the speech input  108 . Similarly, the relative tones and stresses of words used within the speech input  108  can be emulated by the prosody component  150 .  
         [0030]    The following example, which assumes that the text-to-speech application  140  is a concatenative text-to-speech application, illustrates how relative tones and stresses within the speech input  108  can be used to alter the speech output  158 . A concatenative text-to-speech application can generate speech based upon a set of stored phonemes and/or sub-phonemes. In one configuration, a costing algorithm can be used to determine which of the available phonemes used by the concatenative text-to-speech application is to be selected during speech generation. The costing algorithm can make this determination using various weighed factors, which can include tonal factors and factors for word stress. The prosody component  150  can alter baseline weighted factors based upon the prosodic characteristics  125  extracted from the speech input  108 . In another configuration that uses a concatenative text-to-speech application, phoneme and/or sub-phonemes can be extracted from the speech input  108  and added to the pool of phonemes used by the concatenative text-to-speech application. In both configurations, the prosody component  150  can be capable of emulating tonal, stress, and other prosodic characteristics of the speech input  108 . It should be appreciated that other output adjustment methods can utilized by the prosody component  150  and the invention is not intended to be limited to the aforementioned adjustment methods.  
         [0031]    In one embodiment, the prosody component  150  can apply prosodic characteristics  127  from the prosodic characteristic store  135  only when the prosodic characteristic store  135  contains words matching words in the output text string  142 . For example, a customer&#39;s name or account number that was contained within the speech input  108  can be included in the output text string  142 . In such a situation, the recorded prosodic characteristics  127  for the name or account number can be utilized by the prosody component  150 .  
         [0032]    In another embodiment, the prosody component  150  can receive more generalized prosodic characteristics  127  from the prosodic characteristic store  135  and utilize these generalized prosodic characteristics  127  regardless of the individual words from the output text string  142  being processed by the prosody component  150 . For example, the speed before and after words and the pauses before and after each word of the speech input  108  can form general patterns, such as longer pauses before nouns than articles and quicker pronunciation of verbs than average, that can be emulated by the prosody component  150 .  
         [0033]    In yet another embodiment, the speech input  108  can be analyzed to determine a speaker&#39;s proficiency and/or comfort level in the language being spoken. For example, if the speech input  108  includes “I vuld like to vly to Orrlatdo”, the speech recognition application  105  can assign a relatively low language proficiently level to the speaker. This proficiency level can be stored within the prosodic characteristic store  135  and accessed by the text-to-speech application  140 . Based upon the language proficiency level, the text-to-speech application  140  can adjust the speech output  158 . For example, whenever a speaker has a low language proficiency level, the text-to-speech application  140  can be adjusted to maximize clarity, thereby producing slower, less naturally sounding speech output  158 .  
         [0034]    The synthesis component  155  can interpret annotated textual data from the prosody component  150  and generate an audible signal that corresponds to the annotated textual data. As the synthesis component  155  is the speech generating component, the annotated data of the prosodic component  150  can be applied when the synthesis component can interpret and convert the annotated output. Accordingly, possible prosodic characteristics  127  can be limited by the approach and algorithms utilized by the synthesis component  155 . Nevertheless, any synthesis approach can be utilized within the system  100 . For example, the synthesis component  155  can utilize a concatenative approach, a rule based approach, a combined approach that uses both rule based and concatenative synthesis, as well as any other synthesis approach capable of accepting input from the prosody component  150 .  
         [0035]    Notably, in one embodiment, the speech input  108  or portions thereof can be stored within the prosodic characteristic store  135 . The text-to-speech application  140  can then utilize the stored audio when generating synthetic speech. For example, portions of the stored audio can be concatenated with synthetically generated speech segments to ultimately generate the speech output  158 .  
         [0036]    [0036]FIG. 2 is a flow chart illustrating a method  200  for extracting and subsequently applying prosodic characteristics. The method  200  can be performed in the context of a system that receives a speech input and returns a synthetically generated speech output. The method  200  can begin in step  205  where a speech input is received. The speech input can represent a user response to a posed question, such as a request and accompanying response for a credit card number. In step  210 , the speech input can be sent to a speech recognition application.  
         [0037]    In step  215 , prosodic characteristics of the speech input can be detected. Prosodic characteristics can relate to audible changes in pitch, loudness, and syllable length. Moreover, prosodic characteristics can create a segmentation of a speech chain into groups of syllables. In other words, prosodic characteristics can be used to form groupings of syllables and words into larger groupings. Any prosodic characteristics inherent in speech that can be recorded can be detected during this step. For example, the speed before and after each word, the pauses before and after each word, the rhythm of a group of words, the relative tones of each word, and the stresses of each syllable, syllable combination, or word can be detected. This list of prosodic characteristics is not exhaustive and other prosodic characteristics such as intonation and accent can be detected during this step.  
         [0038]    In step  220 , detected prosodic characteristics can be quantified and stored so that speech input from a speaker can be used during speech generation. While in one embodiment, prosodic characteristics can be detected and stored for each word within the speech input, other embodiments can record prosodic characteristics for selected words. For instance, in one embodiment, the detection of a proper noun within the speech input can trigger the collection of prosodic characteristics. Notably, proper nouns, such as people, places, and things can be especially difficult to accurately synthesize due to the many exceptions in their pronunciations. In another embodiment, all detected words having more than one syllable can trigger the collection of prosodic characteristics.  
         [0039]    In step  225 , once the speech recognition process has completed, a back-end system can perform computing functions triggered by a textual input string that results from the speech input. For example, an IVR system can determine whether an input represents a valid customer account number or not. In step  230 , the back-end system can generate an output text string and determine that this string should be conveyed to a user as speech. For example, an, IVR system can generate a confirmation response to confirm a users last input, such as a textual question, “You entered XYZ for your account number, is this correct?” 
         [0040]    In step  235 , the back-end system can initiate a text-to-speech process for the output text string. In step  240 , the text-to-speech application can determine if the output text string should utilize previously extracted prosodic characteristics. Prosodic characteristics can be used for words that were within the speech input and are being repeated within the output and/or can be more generally extrapolated from the input and applied to newly generated words within the output. For example, one embodiment can choose to utilize stored prosodic characteristics only if the speech input contained a previously stored proper noun and that proper noun is repeated in the speech output. In another embodiment, the text-to-speech application can utilize user specific prosodic characteristics for the entire generated speech output.  
         [0041]    In step  242 , characteristics of the speech input can be examined to determine if special handling is warranted. For example, the speech input can indicate the relative language proficiency level of the speaker. If a speaker&#39;s input indicates a low language proficiency level, then output can be adjusted to maximize clarity, which may decrease the pace of generated speech. In another example, the speech input can indicate that a speaker is in a heightened emotional state, such as frantic or angry. If a speaker is frantic, then the pace of the generated speech can be increased. If the speaker is angry, then the text-to-speech application can be adjusted to generate speech that is conciliatory or soothing.  
         [0042]    In step  245 , the text-to-speech application can utilize the previously stored prosodic characteristics when generating speech output. The method  200  either can integrate the stored prosodic characteristics as part of the normal generation of prosodic characteristics for the output, or the method  200  can perform an additional routine that enhances already generated prosodic characteristics. Accordingly, in one embodiment, the method can be implemented as a plug-in component that can be capable of operating with existing text-to-speech applications. In step  250 , the text-to-speech process can result in a speech output that can be conveyed as a digital signal to a desired location or audibly played for a user of the method.  
         [0043]    It should be noted that within method  200 , prosodic characteristics can be stored temporarily for a particular session and/or can be stored for significant periods of time. Accordingly, the text-to-speech application can utilize archived prosodic characteristics recorded during interactive user sessions other than the present one. For example, a user can initiate a first session in which his or her name is received by an IVR system and prosodic characteristics for the name stored. In a second session with the IVR system, the stored prosodic characteristics for the name can be utilized. For instance, when the user enters an account number in the second session, the IVR system can provide a response, such as “Is this Mr. Smith calling about account 321?” where previously stored prosodic characteristics for Mr. Smith&#39;s name can be used to generate the speech output.  
         [0044]    The present invention can be realized in hardware, software, or a combination of hardware and software. The present invention can be realized in a centralized fashion in one computer system or in a distributed fashion where different elements are spread across several interconnected computer systems. Any kind of computer system or other apparatus adapted for carrying out the methods described herein is suited. A typical combination of hardware and software can be a general-purpose computer system with a computer program that, when being loaded and executed, controls the computer system such that it carries out the methods described herein.  
         [0045]    The present invention also can be embedded in a computer program product, which comprises all the features enabling the implementation of the methods described herein, and which when loaded in a computer system is able to carry out these methods. Computer program in the present context means any expression, in any language, code or notation, of a set of instructions intended to cause a system having an information processing capability to perform a particular function either directly or after either or both of the following: a) conversion to another language, code or notation; b) reproduction in a different material form.  
         [0046]    This invention can be embodied in other forms without departing from the spirit or essential attributes thereof. Accordingly, reference should be made to the following claims, rather than to the foregoing specification, as indicating the scope of the invention.