An end-pointer determines a beginning and an end of a speech segment. The end-pointer includes a voice triggering module that identifies a portion of an audio stream that has an audio speech segment. A rule module communicates with the voice triggering module. The rule module includes a plurality of rules used to analyze a part of the audio stream to detect a beginning and an end of the audio speech segment. A consonant detector detects occurrences of a high frequency consonant in the portion of the audio stream.

BACKGROUND OF THE INVENTION

1. Technical Field

These inventions relate to automatic speech recognition, and more particularly, to systems that identify speech from non-speech.

2. Related Art

Automatic speech recognition (ASR) systems convert recorded voice into commands that may be used to carry out tasks. Command recognition may be challenging in high-noise environments such as in automobiles. One technique attempts to improve ASR performance by submitting only relevant data to an ASR system. Unfortunately, some techniques fail in non-stationary noise environments, where transient noises like clicks, bumps, pops, coughs, etc trigger recognition errors. Therefore, a need exists for a system that identifies speech in noisy conditions.

SUMMARY

An end-pointer determines a beginning and an end of a speech segment. The end-pointer includes a voice triggering module that identifies a portion of an audio stream that has an audio speech segment. A rule module communicates with the voice triggering module. The rule module includes a plurality of rules used to analyze a part of the audio stream to detect a beginning and end of an audio speech segment. A consonant detector detects occurrences of a high frequency consonant in the portion of the audio stream.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

ASR systems are tasked with recognizing spoken commands. These tasks may be facilitated by sending voice segments to an ASR engine. A voice segment may be identified through end-pointing logic. Some end-pointing logic applies rules that identify the duration of consonants and pauses before and/or after a vowel. The rules may monitor a maximum duration of non-voiced energy, a maximum duration of continuous silence before a vowel, a maximum duration of continuous silence after a vowel, a maximum time before a vowel, a maximum time after a vowel, a maximum number of isolated non-voiced energy events before a vowel, and/or a maximum number of isolated non-voiced energy events after a vowel. When a vowel is detected, the end-pointing logic may follow a signal-to-noise (SNR) contour forward and backward in time. The limits of the end-pointing logic may occur when the amplitude reaches a predetermined level which may be zero or near zero. While searching, the logic identifies voiced and unvoiced intervals to be processed by an ASR engine.

Some end-pointers examine one or more characteristics of an audio stream for a triggering characteristic. A triggering characteristic may identify a speech interval that includes voiced or unvoiced segments. Voiced segments may have a near periodic structure in the time-domain like vowels. Non-voiced segments may have a noise-like structure (nonperiodic) in the time domain like a fricative. The end-pointers analyze one or more dynamic aspects of an audio stream. The dynamic aspects may include: (1) characteristics that reflect a speaker's pace (e.g., rate of speech), pitch, etc.; (2) a speaker's expected response (such as a “yes” or “no” response); and/or (3) environmental characteristics, such as a background noise level, echo, etc.

FIG. 1is a block diagram of a speech end-pointing system. The end-pointing system100encompasses hardware and/or software running on one or more processors on top of one or more operating systems. The end-pointing system100includes a controller102and a processor104linked to a remote (not shown) and/or local memory106. The processor104accesses the memory106through a unidirectional or a bidirectional bus. The memory106may be partitioned to store a portion of an input audio stream, a rule module108, and support files that detect the beginning and/or end of an audio segment, and a voicing analysis module116. When read by the processor104, the voicing analysis module116may detect a triggering characteristic that identifies a speech interval. When integrated within or when a unitary part of controller serving an ASR engine, the speech interval may be processed when the ASR code118is read by the processor104.

The local or remote memory106may buffer audio data received before or during an end-pointing process. The processor104may communicate through an input/output (I/O) interface110that receives input from devices that convert sound waves into electrical, optical, or operational signals114. The I/O110may transmit these signals to devices112that convert signals into sound. The controller104and/or processor104may execute the software or code that implements each of the processes described herein including those described inFIGS. 3,4,10, and13.

FIG. 2illustrates an end-pointer system100within a vehicle200. The controller102may be programmed within or linked to a vehicle on-board computer, such as an electronic control unit, an electronic control module, and/or a body control module. Some systems may be located remote from the vehicle. Each system may communicate with vehicle logic through one or more serial or parallel buses or wireless protocols. The protocols may include one or more J1850VPW, J1850PWM, ISO, ISO9141-2, ISO14230, CAN, High Speed CAN, MOST, LIN, IDB-1394, IDB-C, D2B, Bluetooth, TTCAN, TTP, or other protocols such as a protocol marketed under the trademark FlexRay.

FIG. 3is a flowchart of a speech end-pointer process. The process operates by dividing an input audio stream into discrete segments or packages of information, such as frames. The input audio stream may be analyzed on a frame-by-frame basis. In some systems, the fixed or variable length frames may be comprised of about 10 ms to about 100 ms of audio input. The system may buffer a predetermined amount of data, such as about 350 ms to about 500 ms audio input data, before processing is carried out. An energy detector302(or process) may be used to detect voiced and unvoiced sound. Some energy detectors and processes compare the amount of energy in a frame to a noise estimate. The noise estimate may be constant or may vary dynamically. The difference in decibels (dB), or ratio in power, may be an instantaneous signal to noise ratio (SNR).

Initially, the process designates some or all of the initial frames as not speech304. When energy is detected, voicing analysis of the current frame or, designated framenoccurs at306. The voicing analysis described in U.S. Ser. No. 11/131,150, filed May 17, 2005, which is incorporated herein by reference, may be used. The voicing analysis monitors triggering characteristics that may be present in framen. The voicing analysis may detect higher frequency consonants such as an “s” or “x” in a framen. Alternatively, the voicing analysis may detect vowels. To further explain the process, a vowel triggering characteristic is further described.

Voicing analysis detects vowels in frames inFIG. 3. A process may identify vowels through a pitch estimator. The pitch estimator may look for a periodic signal in a frame to identify a vowel. Alternatively, the pitch estimator may look for a predetermined threshold at a predetermined frequency to identify vowels.

When the voicing analysis detects a vowel in framen, the framenis marked as speech at310. The system then processes one or more previous frames. A previous frame may be an immediate preceding frame, framen−1at312. The system may determine whether the previous frame was previously marked as speech at314. If the previous frame was marked as speech (e.g., answer of “Yes” to block314), the system analyzes a new audio frame at304. If the previous frame was not marked as speech (e.g., answer of “No” to314), the process applies one or more rules to determine whether the frame should be marked as speech.

Block316designates decision block “Outside EndPoint” that applies one or more rules to determine when the frame should be marked as speech. The rules may be applied to any part of the audio segment, such as a frame or a group of frames. The rules may determine whether the current frame or frames contain speech. If speech is detected, the frame is designated within an end-point. If not, the frame is designated outside of the endpoint.

If a framen−1is outside of the end-point (e.g., no speech is present), a new audio frame, framen+1, may be processed. It may be initially designated as non-speech, at block304. If the decision at316indicates that framen−1is within the end-point (e.g., speech is present), then framen−1is designated or marked as speech at318. The previous audio stream is then analyzed, until the last frame is read from a local or remote memory at320.

FIG. 4is an exemplary detailed process of316. Act316may apply one or more rules. The rules relate to aspects that may identify the presence and/or absence of speech. InFIG. 4, the rules detect verbal segments by identifying a beginning and/or an endpoint of a spoken utterance. Some rules are based on analyzing an event (e.g. voiced energy, un-voiced energy, an absence/presence of silence, etc.). Other rules are based on a combination of events (e.g. un-voiced energy followed by silence followed by voiced energy, voiced energy followed by silence followed by unvoiced energy, silence followed by un-voiced energy followed by silence, etc.).

The rules may examine transitions into energy events from periods of silence or from periods of silence into energy events. A rule may analyze the number of transitions before a vowel is detected; another rule may determine that speech may include no more than one transition between an unvoiced event or silence and a vowel. Some rules may analyze the number of transitions after a vowel is detected with a rule that speech may include no more than two transitions from an unvoiced event or silence after a vowel is detected.

One or more rules may be based on the occurrence of one or multiple events (e.g. voiced energy, un-voiced energy, an absence/presence of silence, etc.). A rule may analyze the time preceding an event. Some rules may be triggered by the lapse of time before a vowel is detected. A rule may expect a vowel to occur within a variable range such as about a 300 ms to 400 ms interval or a rule may expect a vowel to be detected within a predetermined time period (e.g., about 350 ms in some processes). Some rules determine a portion of speech intervals based on the time following an event. When a vowel is detected a rule may extend a speech interval by a fixed or variable length. In some processes the time period may comprise a range (e.g., about 400 ms to 800 ms in some processes) or a predetermined time limit (e.g., about 600 ms in some processes).

Some rules may examine the duration of an event. The rules may examine the duration of a detected energy (e.g., voiced or unvoiced) or the lack of energy. A rule may analyze the duration of continuous unvoiced energy. A rule may establish that continuous unvoiced energy may occur within a variable range (e.g., about 150 ms to about 300 ms in some processes), or may occur within a predetermined limit (e.g., about 200 ms in some processes). A rule may analyze the duration of continuous silence before a vowel is detected. A rule may establish that speech may include a period of continuous silence before a vowel is detected within a variable range (e.g., about 50 ms to about 80 ms in some processes) or at a predetermined limit (e.g., about 70 ms in some processes). A rule may analyze the time duration of continuous silence after a vowel is detected. Such a rule may establish that speech may include a duration of continuous silence after a vowel is detected within a variable range (e.g., about 200 ms to about 300 ms in some processes) or a rule may establish that silence occurs across a predetermined time limit (e.g., about 250 ms in some processes).

At402, the process determines if a frame or group of frames has an energy level above a background noise level. A frame or group of frames having more energy than a background noise level may be analyzed based on its duration or its relationship to an event. If the frame or group of frames does not have more energy than a background noise level, then the frame or group of frames may be analyzed based on its duration or relationship to one or more events. In some systems the events may comprise a transition into energy events from periods of silence or a transition from periods of silence into energy events.

When energy is present in the frame or a group of frames, an “energy” counter is incremented at block404. The “energy” counter tracks time intervals. It may be incremented by a frame length. If the frame size is about 32 ms, then block404may increment the “energy” counter by about 32 ms. At406, the “energy” counter is compared to a threshold. The threshold may correspond to the continuous unvoiced energy rule which may be used to determine the presence and/or absence of speech. If decision406determines that the threshold was exceeded, then the frame or group of frames are designated outside the end-point (e.g. no speech is present) at408at which point the system jumps back to304ofFIG. 3. In some alternative processes multiple thresholds may be evaluated at406.

If the time threshold is not exceeded by the “energy” counter at406, then the process determines if the “noenergy” counter exceeds an isolation threshold at410. The “noenergy” counter418may track time and is incremented by the frame length when a frame or group of frames does not possess energy above a noise level. The isolation threshold may comprise a threshold of time between two plosive events. A plosive relates to a speech sound produced by a closure of the oral cavity and subsequent release accompanied by a burst of air. Plosives may include the sounds /p/ in pit or /d/ in dog. An isolation threshold may vary within a range (e.g., such as about 10 ms to about 50 ms) or may be a predetermined value such as about 25 ms. If the isolation threshold is exceeded, an isolated unvoiced energy event (e.g., a plosive followed by silence) was identified, and “isolatedevents” counter412is incremented. The “isolatedevents” counter412is incremented in integer values. After incrementing the “isolatedevents” counter412, “noenergy” counter418is reset at block414. The “isolatedevents” counter may be reset due to the energy found within the frame or group of frames analyzed. If the “noenergy” counter418does not exceed the isolation threshold, the “noenergy” counter418is reset at block414without incrementing the “isolatedevents” counter412. The “noenergy” counter418is reset because energy was found within the frame or group of frames analyzed. When the “noenergy” counter418is reset, the outside end-point analysis designates the frame or group of frames analyzed within the end-point (e.g. speech is present) by returning a “NO” value at416. As a result, the system marks the analyzed frame(s) as speech at318or322ofFIG. 3.

Alternatively, if the process determines that there is no energy above the noise level at402then the frame or group of frames analyzed contain silence or background noise. In this condition, the “noenergy” counter418is incremented. At420, the process determines if the value of the “noenergy” counter exceeds a predetermined time threshold. The predetermined time threshold may correspond to the continuous non-voiced energy rule threshold which may be used to determine the presence and/or absence of speech. At420, the process evaluates the duration of continuous silence. If the process determines that the threshold is exceeded by the value of the “noenergy” counter at420, then the frame or group of frames are designated outside the end-point (e.g. no speech is present) at block408. The process then proceeds to304ofFIG. 3where a new frame, framen+1, is received and marked as non-speech. Alternatively, multiple thresholds may be evaluated at420.

If no time threshold is exceeded by the value of the “noenergy” counter418, then the process determines if the maximum number of allowed isolated events has occurred at422. The maximum number of allowed isolated events is a configurable or programmed parameter. If grammar is expected (e.g. a “Yes” or a “No” answer) the maximum number of allowed isolated events may be programmed to “tighten” the end-pointer's interval or band. If the maximum number of allowed isolated events is exceeded, then the frame or frames analyzed are designated as being outside the end-point (e.g. no speech is present) at block408. The system then jumps back to block304where a new frame, framen+1, is processed and marked as non-speech.

If the maximum number of allowed isolated events is not reached, “energy” counter404is reset at block424. “Energy” counter404may be reset when a frame of no energy is identified. When the “energy” counter404is reset, the outside end-point analysis designates the frame or frames analyzed inside the end-point (e.g. speech is present) by returning a “NO” value at block416. The process then marks the analyzed frame as speech at318or322ofFIG. 3.

FIGS. 5-9show time series of a simulated audio stream, characterization plots of these signals, and spectrographs of the corresponding time series signals. The simulated audio stream502ofFIG. 5comprises the spoken utterances “NO”504, “YES”506, “NO”504, “YES”506, “NO”504, “YESSSSS”508, “NO”504, and a number of “clicking” sounds510. The clicking sounds may represent the sound heard when a vehicle's turn signal is engaged. Block512illustrates various characterization plots for the time series audio stream. Block512displays the number of samples along the x-axis. Plot514is a representation of an end-pointer marking a speech interval. When plot514has little or no amplitude, the end-pointer has not detected a speech segment. When plot514has measurable amplitude the end-pointer detected speech that may be within the bounded interval. Plot516represents the energy detected above a background energy level. Plot518represents a spoken utterance in the time domain. Block520illustrates a spectral representation of the audio stream in block502.

Block512illustrates how the end-pointer may respond to an input audio stream. InFIG. 5, end-pointer plot514captures the “NO”504and the “YES”506signals. When the “YESSSSS”508is processed, the end-pointer plot514captures a portion of the trailing “S”, but when it reaches a maximum time period after a vowel or a maximum duration of continuous non-voiced energy has been exceeded (by rule) the end-pointer truncates a portion of the signal. The rule-based end-pointer sends the portion of the audio stream that is bound by end-pointer plot514to an ASR engine. In block512, andFIGS. 6-9, the portion of the audio stream sent to an ASR engine may vary with the selected rule.

InFIG. 5, the detected “clicks”510have energy. Because no vowel was detected within that interval, the end-pointer does not capture the energy. A pause is declared which is not sent to the ASR engine.

FIG. 6magnifies a portion of an end-pointed “NO”504. The lag in the spoken utterance plot518may be caused by time smearing. The magnitude of518reflects period in which energy is detected. The energy of the spoken utterance518is nearly constant. The passband of the end-pointer514begins when speech energy is detected and cuts off by rule. A rule may determine the maximum duration of continuous silence after a vowel or the maximum time following the detection of a vowel. InFIG. 6, the audio segment sent to an ASR engine comprises approximately 3150 samples.

FIG. 7magnifies a portion of an end-pointed “YES”506. The lag in the spoken utterance plot518may be caused by time smearing. The passband of the end-pointer514begins when speech energy is detected and continues until the energy falls off from the random noise. The upper limit of the passband may be set by a rule that establishes the maximum duration of continuous non-voiced energy or by a rule that establishes the maximum time after a vowel is detected. InFIG. 7, the portion of the audio stream that is sent to an ASR engine comprises approximately 5550 samples.

FIG. 8magnifies a portion of one end-pointed “YESSSSS”508. The end-pointer accepts the post-vowel energy as a possible consonant for a predetermined period of time. When the period lapses, a maximum duration of continuous non-voiced energy rule or a maximum time after a vowel rule may be applied limiting the data passed to an ASR engine. InFIG. 8, the portion of the audio stream that is sent to an ASR engine comprises approximately 5750 samples. Although the spoken utterance continues for an additional 6500 samples, in one system, the end-pointer truncates the sound segment by rule.

FIG. 9magnifies an end-pointed “NO”504and several “clicks”510. InFIG. 9, the lag in the spoken utterance plot518may be caused by time smearing. The passband of the end-pointer514begins when speech energy is detected. A click may be included within end-pointer514because the system detected energy above the background noise threshold.

Some end-pointers determine the beginning and/or end of a speech segment by analyzing a dynamic aspect of an audio stream.FIG. 10is a partial process that analyzes the dynamic aspect of an audio segment. An initialization of global aspects occurs at1002. Global aspects may include selected characteristics of an audio stream such as characteristics that reflect a speaker's pace (e.g., rate of speech), pitch, etc. The initialization at1004may be based on a speaker's expected response (such as a “yes” or “no” response); and/or environmental characteristics, such as a background noise level, echo, etc.

The global and local initializations may occur at various times throughout system operation. The background noise estimations (local aspect initialization) may occur during nonspeech intervals or when certain events occur such as when the system is powered up. The pace of a speaker's speech or pitch (global initialization) and monitoring of certain responses (local aspect initialization) may be initialized less frequently. Initialization may occur when an ASR engine communicates to an end-pointer or at other times.

During initialization periods1002and1004, the end-pointer may operate at programmable default thresholds. If a threshold or timer needs to be change, the system may dynamically change the thresholds or timing values. In some systems, thresholds, times, and other variables may be loaded into an end-pointer by reading specific or general user profiles from the system's local memory or a remote memory. These values and settings may also be changed in real-time or near real-time. If the system determines that a user speaks at a fast pace, the duration of certain rules may be changed and retained within the local or remote profiles. If the system uses a training mode, these parameters may also be programmed or set during a training session.

The operation of some dynamic end-pointer processes may have similar functionality to the processes described inFIGS. 3 and 4. Some dynamic end-pointer processes may include one or more thresholds and/or rules. In some applications the “Outside Endpoint” routine, block316is dynamically configured. If a large background noise is detected, the noise threshold at402may be raised dynamically. This dynamic re-configuration may cause the dynamic end-pointer to reject more transients and non-speech Sounds. Any threshold utilized by the dynamic end-pointer may be dynamically configured.

An alternative end-pointer system includes a high frequency consonant detector or s-detector that detects high-frequency consonants. The high frequency consonant detector calculates the likelihood of a high-frequency consonant by comparing a temporally smoothed SNR in a high-frequency band to a SNR in one or more low frequency bands. Some systems select the low frequency bands from a predetermined plurality of lower frequency bands (e.g., two, three, four, five, etc. of the lower frequency bands). The difference between these SNR measurements is converted into a temporally smoothed probability through probability logic that generates a ratio between about zero and one hundred that predicts the likelihood of a consonant.

FIG. 11is a diagram of a consonant detector1100that may be linked to or may be a unitary part of an end-pointing system. A receiver or microphone captures the sound waves during voice activity. A Fast Fourier Transform (FFT) element or logic converts the time-domain signal into a frequency domain signal that is broken into frames1102. A filter or noise estimate logic predicts the noise spectrum in each of a plurality of low frequency bands1104. The energy in each noise estimate is compared to the energy in the high frequency band of interest through a comparator that predicts the likelihood of an /s/ (or unvoiced speech sound such as /f/, /th/, /h/, etc., or in an alternate system, a plosive such as /p/, /t/, /k/, etc.) in a selected band1106. If a current probability within a frequency band varies from the previous probability, one or more leaky integrators and/or logic may modify the current probability. If the current probability exceeds a previous probability, the current probability is adapted by the addition of a smoothed difference (e.g., a difference times a smoothing factor) between the current and previous probabilities thorough an adder and multiplier1109. If a current probability is less than the previous probability a percentage difference of the current and previous probabilities is added to the current probability by an adder and multiplier1110. While a smoothing factor and percentage may be controlled and/or programmed with each application of the consonant detector; in some systems, the smoothing factor is much smaller than the applied percentage. The smoothing factor may comprise an average difference in percent across an “n” number of audio frames. “n” may comprise one, two, three or more integer frames of audio data.

FIG. 12is a partial diagram of the consonant detector1200. The average probability of two, three, or more (e.g., “n” integer) audio frames is compared to the current probability of an audio frame through a weighted comparator1202. If the ratio of consecutive ratios (e.g., %framen−2/%framen−1; %framen−1/%framen) has an increasing trend, an /s/ (or other unvoiced sound or plosive) is detected. If the ratio of consecutive ratios shows a decreasing trend an end-point of the speech interval may be declared.

One process that may adjust the voice thresholds may be based on the detection of unvoiced speech, plosives, or a consonant such as an /s/. InFIG. 13, if an /s/ is not detected in a current or previous frame and the voice thresholds have not changed during a predetermined period, the current voice thresholds and frame numbers are written to a local and/or remote memory1302before the voice thresholds are programmed to a predetermined level1304. Because voice sound may have a more prominent harmonic structure than unvoiced sound and plosives, the voice thresholds may be programmed to a lower level. In some processes the voice thresholds may be dropped within a range of approximately 49% to about 76% of the current voice threshold to make the comparison more sensitive to weak harmonic structures. If an /s/ (or another unvoiced sound or plosive) is detected1306, the voice thresholds are increased across a programmed number of audio frames1308before it is compared to the current thresholds1310and written to the local and/or remote memory. If the increased threshold and current thresholds are the same, the process ends1312. Otherwise, the process analyzes more frames. If an /s/ is detected1306, the process enters a wait state1314until an /s/ is no longer detected. When an /s/ is no longer detected the process stores the current frame number1316in the local and/or the remote memory and raises the voice thresholds across a programmed number of audio frames1318. When the raised threshold and current thresholds are the same1310, the process ends1312. Otherwise, the process analyzes another frame of audio data.

In some processes the programmed number of audio frames comprises the difference between the originally stored frame number and the current frame number. In an alternative process, the programmed frame number comprises the number of frames occurring within a predetermined time period (e.g., may be very short such as about 100 ms). In these processes the voice threshold is raised to the previously stored current voice threshold across that time period. In an alternative process, a counter tracks the number of frames processed. The alternative process raises the voice threshold across a count of successive frames.

FIG. 14exemplifies spectrograms of a voiced segment spoken by a male (a) and a female (b). Both segments were spoken in a substantially noise free environment and show the short duration of a vowel preceded and followed by the longer duration of high frequency consonants. Note the strength of the low frequency harmonics in (a) in comparison to the harmonic structure in (b).FIG. 15exemplifies a spectrogram of a voiced segment of the numbers 6, 1, 2, 8, and 1 spoken in French. The articulation of the number 6 includes a short duration vowel preceded and followed by longer duration high-frequency consonant. Note that there is substantially less energy contained in the harmonics of the number 6 than in the other digits.FIG. 16exemplifies a magnified spectrogram of the number 6. In this figure the duration of the consonants are much longer than the vowel. Their approximate occurrence is annotated near the top of the figure. InFIG. 16the consonant that follows the vowel is approximately 400 ms long.

FIG. 17exemplifies spectrograms of a voiced segment positioned above an output of an /s/ (or consonant detector) detector. The /s/ detector may identify more than the occurrence of an /s/ Notice how other high-frequency consonants such as the /s/ and /x/ in the numbers 6 and 7 and the /t/ in the numbers 2 and 8 are detected and accurately located by the /s/ detector.FIG. 18exemplifies spectrogram of a voiced segment positioned above an end-point interval without an /s/ or consonant detection. The voiced segment comprises a French string spoken in a high noise condition. Notice how only the number 2 and 5 are detected and correctly end-pointed while other digits are not identified.FIG. 19exemplifies the same voice segment ofFIG. 18positioned above end-point intervals adjusted by the /s/ or consonant detection. In this case each of the digits is captured within the interval.

FIG. 20exemplifies spectrograms of a voiced segment positioned above an end-point interval without /s/ or consonant detection. In this example the significant energy in a vowel of the number 6 (highlighted by the arrow) trigger an end-point interval that captures the remaining sequence. If the six had less energy there is a probability that the entire segment would have been missed.FIG. 21exemplifies the same voice segment ofFIG. 20positioned above end-point intervals adjusted by the /s/ or consonant detection. In this case each of the digits is captured within the interval.

The methods shown inFIGS. 3,4,10,13, may be encoded in a signal bearing medium, a computer readable medium such as a memory, programmed within a device such as one or more integrated circuits, or processed by a controller or a computer. If the methods are performed by software, the software may reside in a memory partitioned with or interfaced to the rule module108, voice analysis module116, ASR engine118, a controller, or other types of device interface. The memory may include an ordered listing of executable instructions for implementing logical functions. Logic may comprise hardware, software, or a combination. A logical function may be implemented through digital circuitry, through source code, through analog circuitry, or through an analog source such as through an electrical, audio, or video signal. The software may be embodied in any computer-readable or signal-bearing medium, for use by, or in connection with an instruction executable system, system, or device. Such a system may include a computer-based system, a processor-containing system, or another system that may selectively fetch instructions from an instruction executable system, system, or device that may also execute instructions.

While various embodiments of the inventions have been described, it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible within the scope of the inventions. Accordingly, the inventions are not to be restricted except in light of the attached claims and their equivalents.