Speech recognition method and apparatus for use therein

An apparatus and method for selective distributed speech recognition includes a dialog manager (104) that is capable of receiving a grammar type indicator (170). The dialog manager (104) is capable of being coupled to an external speech recognition engine (108), which may be disposed on a communication network (142). The apparatus and method further includes an audio receiver (102) coupled to the dialog manager (104) wherein the audio receiver (104) receives a speech input (110) and provides an encoded audio input (112) to the dialog manager (104). The method and apparatus also includes an embedded speech recognition engine (106) coupled to the dialog manager (104), such that the dialog manager (104) selects to distribute the encoded audio input (112) to either the embedded speech recognition engine (106) or the external speech recognition engine (108) based on the corresponding grammar type indicator (170).

A speech recognizer suitable for distributed speech recognition is robust to missing speech feature vectors. Speech is transmitted via a packet switched network in the form of basic feature vectors. Missing feature vectors are detected and replacement feature vectors are estimated by interpolation of received data prior to speech recognition. Features may be converted and interpolation may be accomplished in a spectral domain.

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Phonetic recognition is provided by capturing dynamical behavior and statistical dependencies of the acoustic attributes used to represent a subject speech waveform. A segment based framework is employed. Temporal behavior is modelled explicitly by creating dynamic templates, called tracks, of the acoustic attributes used to represent the speech waveform, and by generating the estimation of the acoustic spatio-temporal correlation structure. An error model represents this estimation as the temporal and spatial correlations between the input speech waveform and track generated speech segment. Models incorporating these two components (track and error estimation) are created for both phonetic units and for phonetic transitions. Phonetic contextual influences are accounted for by merging context-dependent tracks and pooling error statistics over the different contexts. This allows for a large number of contextual models without compromising the robustness of the statistical parameter estimates. The transition models also supply contextual information.

Speech parameters (Ph and Pl) are derived for consonant classification and recognition by separating a speech signal into Low and High frequency bands, then in each band obtaining the time first-derivative, from which the min-max differences (power dip) are obtained (Ph and Pl). The distribution of Ph and Pl in a two-dimensional plot for a discriminant diagram classifies the consonant phoneme.

Speech signal analysis for data reduction, as stored for synthesis or recognition, is improved by features including: digital spectral analysis; reduction of channel data and bit allocation by selective summation of groups of contiguous data; using the mean average of the log amplitude to find the deviation for each channel; also using the instaneous shape of the mean value for each channel for pairs of adjacent frames, all combined to find a feature ensemble for each pair of adjacent frames.

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A system is disclosed for recognizing a pattern in a collection of data given a context of one or more other patterns previously identified. Preferably the system is a speech recognition system, the patterns are words and the collection of data is a sequence of acoustic frames. During the processing of each of a plurality of frames, for each word in an active vocabulary, the system updates a likelihood score representing a probability of a match between the word and the frame, combines a language model score based on one or more previously recognized words with that likelihood score, and prunes the word from the active vocabulary if the combined score is below a threshold. A rapid match is made between the frames and each word of an initial vocabulary to determine which words should originally be placed in the active vocabulary. Preferably the system enables an operator to confirm the system's best guess as to the spoken word merely by speaking another word, to indicate that an alternate guess by the system is correct by typing a key associated with that guess, and to indicate that neither the best guess nor the alternate guesses was correct by typing yet another key. The system includes other features, including ones for determining where among the frames to look for the start of speech, and a special hardware processor for computing likelihood scores.

In a word speech recognition method which performs pattern matching between unknown speech pattern and multiple reference templates and detects that one of the reference templates which provides the smallest one of distance measures detected between the unknown speech pattern and the reference templates, when the difference d between the speech period length of the unknown speech pattern and the speech period length of a selected reference template exceeds a fixed threshold value e1, partial patterns are extracted from the unknown speech pattern, each starting at a different position, and the minimum one of the distances obtained by pattern matching between these extracted partial patterns and the selected reference template is determined to be the distance between the selected reference template and the unknown speech pattern. When the difference d is in the range of -e2 ≦d≦e1, pattern matching is performed between speech periods of the unknown speech pattern and the reference templates with their variation periods eliminated therefrom at their both ends.

Method and apparatus for continuous speech recognition using a layered, self-adjusting decoder network

Synchronized spontaneous low frequency fluctuations of the so called BOLD signal, as measured by functional Magnetic Resonance Imaging (fMRI), are known to represent the functional connections of different brain areas. Dynamic Time Warping (DTW) distance can be used as a similarity measure between BOLD signals of brain regions as an alternative of the traditionally used correlation coefficient and the usage of the DTW algorithm has further advantages: beside the DTW distance, the algorithm generates the warping path, i.e. the time-delay function between the compared two time-series. In this paper, we propose to use the relative length of the warping path as classification feature and demonstrate that the warping path itself carries important information when classifying patients according to cannabis addiction. We discuss biomedical relevance of our findings as well.

As to guarantee the safe broadcast of radio programs and prevent the problems such as wrong broadcasting, intercut and so on. A comparison model of broadcast audio based on DTW warping path is proposed in this paper. It first solves time delay problem of two audios before comparison by searching for audio synchronization point. Then it extracts the MFCC coefficient to calculate the audio similarity by dynamic time warping (DTW), which obtains the optimal warping path. Finally calculating the length of the warping path within the best matching range as the similarity of two audios. The Experimental results show that the model can accurately judge different types of audio programs with high accuracy.

The present invention comprises a method and system for speech recognition. The method includes the steps of receiving digital data representation of speech having at least one word, which includes a waveform representation of the at least one word. The method includes searching a library containing digital data representation of a plurality of words and matching the digital data representation of the at least one word to digital data representation of a word in the library. The method further includes the steps of determining the number of syllables in the digital data representation of the at least one word, and then providing an ASCII representation of the matched word in response to the number of syllables of the at least one word being identical to the number of syllables of the matched word in the library.

A words phonetic decoding method in automatic speech recognition is considered. The properties of Kullback–Leibler divergence are used to synthesize the estimation of the distribution of divergence between minimum speech units (e.g., single phonemes) inside a single class. It is demonstrated that the minimum variance of the intraphonemic divergence is reached when the phonetic database is tuned to the voice of a single speaker. The estimations are proven by experimental results on the recognition of vowel sounds and isolated words of Russian language.

This paper first introduces the system requirement and the system flow of the auto-plotting system. As the data points needed by the auto-plotting system coming from the remote speech signals, to reach high recognition accuracy, the Hidden Markov Model (HMM) approach was chosen as the speech recognition approach. Then the paper is detailed on the speaker dependent (SD), speaker independent (SI) and speaker adaptive (SA) speech recognition methods. We proposed the n-speech models SD system as the recognition system to gain the highest recognition performance in varying speech environments. However the system required that searching for the optimal model from the database should finish in 5 minutes, so the paper finally describes how the Self-Organizing Map (SOM) was used to pre clustering to the n-speech models, to decrease the time for speech recognition and results evaluation and decrease matching time, Experiments show the n-speech models SD system can select the best-matching model in the limited time and improve the average speech recognition accuracy to 97.2. It ideally suits the system requirements.KeywordsHide Markov ModelSpeech RecognitionCluster SolutionSpeech ModelHigh Recognition AccuracyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.