US 20070136060 A1
A system recognizes speech using lexical lists. The lexical list may have entries that correspond to words or commands. The system includes an interface for receiving voiced speech and a recognition unit that generates string hypotheses based on the voiced speech. The recognition unit assigns a score to each of the string hypotheses. One of the string hypotheses is compared with an entry in the lexical list by a comparison unit. An assignment unit may then assign one of the string hypotheses to an entry in the lexical list.
1. A method of recognizing speech, comprising:
detecting a verbal utterance;
converting the verbal utterance into a speech signal;
digitizing the speech signal;
generating at least two string hypotheses corresponding to the speech signal;
assigning a score to each of the at least two string hypotheses; and
comparing at least one of the string hypotheses with an entry in the lexical list based on the score of the at least one string hypothesis.
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15. A system for recognizing speech using long lexical lists stored in a database, comprising:
a database that stores a lexical list;
an interface in communication with the database that detects a speech signal;
a processor in communication with the interface that digitizes the detected speech signal;
a recognition unit in communication with the processor that generates a plurality of string hypotheses corresponding to the speech signal and assigns a score to each of the plurality of string hypotheses;
a comparison unit that compares at least one of the plurality of string hypotheses with an entry in the lexical list based on the score of the at least one string hypothesis; and,
an assignment unit that assigns the at least one string hypothesis to the entry in the lexical list based on a comparison of the at least one string hypothesis with the entry in the at least one lexical list and the score of the at least one string hypothesis.
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28. A system for recognizing speech using long lexical lists stored in a database, comprising:
a database that stores a lexical list;
an interface in communication with the database that detects a speech signal;
a processor in communication with the interface that digitizes the detected speech signal;
means for generating a plurality of string hypotheses corresponding to the speech signal and for assigning a score to each of the plurality of string hypotheses;
means for comparing at least one of the plurality of string hypotheses with an entry in the lexical list based on the score of the at least one string hypothesis; and,
means for assigning the at least one string hypothesis to the entry in the lexical list based on a comparison of the at least one string hypothesis with the entry in the at least one lexical list and the score of the at least one string hypothesis.
This application claims the benefit of priority from European Application No. 05013168.9, filed Jun. 17, 2005, which is incorporated herein by reference.
1. Technical Field
The invention relates to recognizing speech, in particular, to a system that recognizes speech from lexical lists.
2. Related Art
Some speech recognition systems use variants of phonemes to represent a linguistic word. The variants, known as allophones, may be represented by models that include a sequence of states having a defined transition probability. To recognize a spoken word, the speech recognition system may compute a likely sequence of states through these models. Some speech recognitions systems may infer a correct spelling of a word or sentence. The inference may correspond to acoustic signals that correspond to a finite vocabulary.
A collection of stored words may store too many words for practical applications, especially when the collection is used to access a telephone directory or to initiate a call using voice commands. In these systems, search processes may take an unacceptably long time. In some systems, the recognizing components may not correctly identify words. Recognition may be difficult when lexical lists also include homophones. Some systems mitigate these problems by rank ordering the recognized words and creating N-best lists.
While a comparison between a verbal utterance and entries in a list may result in a ranking, some systems do not provide an indication of reliability. When an unrecognized word is spoken, some systems also unintentionally associate these words with a recognized list.
A system recognizes speech using lexical lists stored in a memory. The system includes an interface that detects speech signals. A processor digitizes the detected speech signals. A recognition unit in communication with the processor generates two or more string hypotheses that correspond to the speech signal and assigns a score to each of the string hypotheses. A comparison unit compares one of the string hypotheses with an entry in the lexical list based on a score. An assignment assigns a string hypothesis to the entry in the lexical list based on a comparison.
Other systems, methods, features and advantages of the invention will be, or will become, apparent to one with skill in the art upon examination of the following figures and detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description, be within the scope of the invention, and be protected by the following claims.
The invention can be better understood with reference to the following drawings and description. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. Moreover, in the figures, like referenced numerals designate corresponding parts throughout the different views.
Due to dramatic improvements in speech recognition technology, high performance speech analysis, recognition algorithms and speech dialog systems are available. Present day speech input capabilities include activities such as voice dialing, call routing, and document preparation. A speech dialog system may be used in various environments. An example of such an environment is a vehicle, where the speech dialog system allows the user to control different devices such as a wireless phone, a car radio, a navigation system or other devices.
Some speech recognition systems are speaker dependent requiring a user to provide samples of his or her speech. Other systems may be speaker independent and may not require the user to provide samples of his or her speech. Where a speech recognition system recognizes words, the recognized words may represent commands to the system and may serve as an input to further linguistic processing. The term “words” may refer to linguistic words, but may also refer to subunits of words, such as syllables, phonemes, allophones, or combinations. A sentence may include any sequence of words, including a sequence of linguistic words.
When the speech signals are detected (Block 100), the detected speech waveforms may be sampled and processed to generate a representation of the speech signals. The verbal utterance detected by the input device may be converted to analog signals and then digitized using an analog-to-digital converter (Block 10). The analog-to-digital converter may be an electronic circuit that converts continuous analog signals to discrete signals. In one system, the analog speech signals may be converted into digital speech signals using pulse code modulation.
Digitizing speech signals may include sampling the analog signals at a rate between about 6.6 kHz and about 20 kHz. Digitizing the speech signals may also include dividing the speech signals into frames at a fixed rate, such as about once every 10-20 ms. Frames may include about 300 samples of about 20 ms duration each. These measurements may be used to search for the most likely word candidate, using the constraints imposed by various models, such as acoustical models, lexical models, language models, or combinations of other similar models.
After converting the analog speech signals into digital speech signals (Block 110), signal processing may be performed on the digital speech signals (Block 120). In
As a feature vector may include a cepstral vector, a determination may be made during signal processing as to whether to perform cepstral encoding (Block 220). If the feature vector is a cepstral vector, then the signal processing may include cepstral encoding to compute the cepstral coefficients (Block 230). The cepstral coefficients may be used to represent the cepstrum, which separates the glottal frequency from the vocal tract resonance of the digitized speech signals. Cepstral encoding may include an inverse Fourier transform of the logarithm of the Fourier transformed detected speech signals digitized by the analog-to-digital conversion (Block 110). Other encoding techniques, such as linear prediction coding, may also be used in addition, or alternatively to, cepstral encoding.
When the cepstral vectors are derived, the speech recognition process may generate an N-best list of string hypotheses (Block 130) as shown in
The scoring of the string hypotheses may include scoring individual phonemes or characters (Block 320). An entire linguistic word hypotheses of characters may also be scored. The scoring of these linguistic words may be based on scores of the characters and phonemes or allophones that comprise the word. Scoring may be based on the acoustic features of phonemes, Hidden Markov Model probabilities, grammar models, or a combination of other models.
In one method, acoustic features of phonemes may be used to determine the score of a string hypothesis. For example, the letter “S” may have a temporal duration of more than 50 ms and may exhibit frequencies above about 44 kHz. These characteristics may be used with statistical classification methods. In another method, the score may represent distance measures indicating how far from or close to a generated vector of an associated word hypothesis is to a specified phoneme. In recognizing sentences, grammar models, including syntactic and semantic information, may be used in scoring individual string hypotheses representing linguistic words.
Different models may be used that generate the N-best list of string hypotheses (Block 330). For example, a Hidden Markov Model (HMM) may be used to generate the N-best list of string hypotheses. An HMM comprises a doubly stochastic model, in which the generation of the underlying phoneme string and the frame-by-frame surface acoustic realizations are both represented probabilistically as Markov processes. During the search process, speech segments may be identified. An alternate approach may be to identify speech segments, then classify the segments and use the segment scores to recognize words.
An alternative to using HMM may be to use text-independent recognition methods based on vector quantization (VQ). Using vector quantization, VQ codebooks having a limited number of representative feature vectors may be used to characterize speaker-specific features. A speaker-specific codebook may be generated by clustering the training feature vectors of each speaker. In the recognition stage, an input utterance may be vector-quantized using the codebook of each reference speaker. The VQ distortion accumulated over the entire input utterance may then be used to make the recognition decision.
In the training phase, reference templates may be generated and identification thresholds may be computed for different phonetic categories. In the identification phase, after the phonetic categorization, a comparison with a reference template for different categories may provide a score for each category. A final or accumulated score may be a weighted linear combination of scores from each category.
The recognizing process (Block 130) of the cepstral vectors provides a scored listing of word or string hypotheses (Block 140). Each recognized word may be evaluated or scored through a probability or some distance measure to each word. Scores may be encoded using characters, numbers, or combinations thereof. For example, if a speech signal is recognized as the letter “F” with a high probability, the score for the letter “S” may be a lower score where recognition of the letter “F” was carried out with a lower reliability.
After scoring the string hypotheses, the method may rank order the string hypotheses (Block 140). As shown in
Based on the scored entries of the ranked list of word hypotheses (Block 140) during the recognizing process (Block 130), comparison with the entries of a lexical list is performed (Block 150). As shown in
As an example, it may be possible to identify the verbal utterance of a consonant, such as the letter “S,” without any or minimal ambiguities. The recognizing result for this consonant may be highly reliable in terms of the employed scoring method. In contrast, a different generated hypothesis, such as the letter “M”, may exhibit a poor scoring. To facilitate and improve the comparison between the recognizing results and the entries in the lexical list, any comparison between the hypothesis letter “M” and the lexical list may be omitted. If a linguistic word is to be identified, words with a leading letter “S” may first be compared to the string hypothesis.
After comparing the scored list with the lexical list (Block 150), the respective generated string hypothesis of the scored listing may be assigned to the entry of the long lexical list that most probably represents the detected speech signals (Block 160). The assignment process may determine which entry of the lexical list most probably corresponds to the detected speech signal. The assignment process may be based on the scores of the string hypotheses. As shown in
As an example, suppose that the score of a consonant, such as the letter “S,” is very high. A high score may indicate that the recognizing result can be regarded as reliable. As in this example, since the letter “S” has a high score, an assignment to an entry in the lexical list representing the letter “S” may be preferred over an assignment to a different consonant have similar acoustical aspects, such as the letter “F.”
In an alternative arrangement, the assignment process may give priority to the score rather than the probability of mistaking a string hypothesis with another string hypothesis. However, utilization of two different criteria, such as the string hypothesis' score and the probability of mistaking one hypothesis for another, may further improve the reliability method for speech recognition. The probability of mistaking one string hypothesis for another, such as mistaking the letter “F” for the letter “N,” may be known a priori or may be determined by testing.
The comparing (Block 150) and/or assigning (Block 160) of
The method of
A “computer-readable medium,” “machine readable medium,” “propagated-signal” medium, and/or “signal-bearing medium” may comprise any device that contains, stores, communicates, propagates, or transports software for use by or in connection with an instruction executable system, apparatus, or device. The machine-readable medium may selectively be, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. A non-exhaustive list of examples of a machine-readable medium would include: an electrical connection “electronic” having one or more wires, a portable magnetic or optical disk, a volatile memory such as a Random Access Memory “RAM” (electronic), a Read-Only Memory “ROM” (electronic), an Erasable Programmable Read-Only Memory (EPROM or Flash memory) (electronic), or an optical fiber (optical). A machine-readable medium may also include a tangible medium upon which software is printed, as the software may be electronically stored as an image or in another format (e.g., through an optical scan), then compiled, and/or interpreted or otherwise processed. The processed medium may then be stored in a computer and/or machine memory.
In one example, the recognition operation (Block 830) is performed on the feature vectors employing a HMM that uses an acoustic model and a language model (810). According to the acoustic model, a sequence of acoustic parameters may be seen as a concatenation of elementary processes described by the HMM. The probability of a sequence of words, such as phonemes, may then be computed by a language model.
Different text-dependent methods may also be used. Such methods may be based on template-matching techniques. Using a template-matching technique, the verbal utterance may be represented by a sequence of feature vectors, such as short-term spectral feature vectors. The time axes of the input utterance and each reference template or reference model of the registered speakers may be aligned using a dynamic time warping (DTW) algorithm. The time axes of the input utterance may be accumulated from the beginning of the input utterance to the end of the input utterance. The degree of similarity between the time axes may then be calculated. However, as an HMM may model statistical variations in spectral features, HMM-based methods may be used as extensions of the DTW-based methods.
A set of string hypotheses may be generated where the hypotheses are listed and scored according to the results of the employed HMM (Block 870). As shown in
Further shown in
The entries of the word hypotheses (840) may then be compared (Block 850) with entries in a database (880) that includes a lexical list. The lexical list may include individual characters, phonemes, linguistic words, or combinations thereof. In one example, each hypothesis for a character is assigned (Block 850) to a character in the lexical list (880). In another example, the string hypothesis consisting of the four characters is assigned to a four-character entry, such as a linguistic word comprising four characters, in the lexical list. If a word consisting of (C1, C3, C3, C4), or (C1, C3, C3, C5), or (C1, C6, C2, C5) is not present in the lexicon but one entry is given by (C1, C2, C3 C4), it may be possible to assign the correct sequence (C1, C2, C3, C4) to the linguistic word hypothesis (C1, C3, C3, C4). As shown in
An interface and input/output control unit (1110) may control the vehicle navigation system (920) through voice commands or other vocalized information. The interface and input/output control unit (1110) may be implemented in software that enables the vehicle navigation system (920) to interact with the other components of the speech recognition system (930), such as the recognition unit (1130) or a comparison and assignment (1150). The interface and input/output control unit (1110) may include an audio input device, such as a microphone or other devices for detecting audio signals, and may further include a pre-processor for processing the detected speech signals through an audio input device. A user may interact with the vehicle navigation system (920) to show the user a route to a destination by speaking the name of the destination. For example, a user may ask for directions to Stuttgart, in Baden-Württemberg, Germany, such as by speaking the word “Stuttgart.” The speech signals representing “Stuttgart” may then be detected and subsequently processed as described in
A recognition unit (1130) may be coupled with the interface and input/output control unit (1110). The recognition unit (1130) may comprise hardware or software. The interface and input/output control unit (1110) may transmit the detected speech signals from the user to the recognition unit (1130). The recognition unit (1110) may then recognize string hypotheses from the detected speech signals. The recognition unit (1130) may be coupled with and supported by a database (1160). If the speaker has trained the system for speech recognition, in addition to controlling the navigation system by speech, driver identification may also be performed.
After recognizing the string hypotheses from the detected speech signals, the recognition unit (1130) may score the string hypotheses. The recognition unit (1130) may provide an ordered list of the scored string hypotheses. These hypotheses may be transmitted to a comparison and assignment (1150). The comparison and assignment (1150) may comprise hardware or software. The comparison and assignment (1150) may comprise a separate comparison unit and a separate assigning unit. For example, the recognition unit (1130) may transmit a set of three string hypotheses, such as “Frukfart,” “Dortmart,” and “Sdotdhord,” to the comparison and assignment (1150). In this example the first character “S” may be regarded as being recognized with a high reliability denoted by a high score.
The comparison and assignment (1150) may then compare the three string hypotheses with entries of a lexical list stored in a management system that stores information, such as a database (1160). The database (1160) may be in communication with the comparison and assignment (1150). In the example described above, since the letter “S” is denoted with a high score, a comparison operation may determine that there is a high probability that the target word starts with the letter “S”. If the score is low, the comparison and assignment unit (1150) may analyze words starting with the letter “F.” It may be known that the recognition unit (1130) might mistake the letter “F” for the letter “S” based on a predetermined probability.
In the current example, the name of the city “Frankfurt” is not regarded as the target word by the comparison and assignment (1150). Rather the correct word “Stuttgart” will be assigned to the most reliable string hypotheses. Alternatively, a successful comparison may be based on a comparison of a substring. A comparison based on a substring may be performed exclusively, alternatively or additionally to comparison on the basis of the entire word hypothesis.
Based on the output from the comparison and assignment (1150), the dialog control (1140) may prompt a request for confirmation, such as “Destination is Stuttgart?,” using a speech output unit (1120). Alternatively, the dialog control may prompt a request for confirmation through a visual output device, such as a liquid crystal display device (not shown) coupled with the dialog control (1140). It may be possible that dialog control (1140) presents visual information and an audio prompt simultaneously. The dialog control (1140) controls the speech output unit (1120) by using the database (1160), which provides the phonetic or textual information about the word(s) and/or sentence(s) outputted a user. The adequate word(s) and/or sentence(s) may depend on the input speech signal provided in a processed form to the recognition unit (1130). In response to the prompt provided by the dialog control (1140), the dialog control (1140) may give navigation instructions by voice via speech output unit (1120) or via a visual output device to guide the driver to the destination “Stuttgart”.
While various embodiments of the invention 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 invention. Accordingly, the invention is not to be restricted except in light of the attached claims and their equivalents.