Method and apparatus for selective distributed speech recognition

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.

The article discusses the system of dialogue control manipulation robots. The analysis of the basic methods of automatic speech recognition, speech understanding, dialogue management, voice response synthesis in dialogue systems has been carried out. Three types of dialogue management are considered as "system initiative", "user initiative" and "combined initiative". A system of object-oriented dialog control of a robot based on the theory of finite state machines with using a deep neural network is proposed. The main difference of the proposed system lies in the separate implementation of the dialogue process and robot’s actions, which is close to the pace of natural dialogue control. This method of constructing a dialogue control robot allows system to automatically correct the result of speech recognition, robot’s actions based on tasks. The necessity of correcting the result of speech recognition and robot’s actions may be caused by the users’ accent, working environment noise or incorrect voice commands. The process of correcting speech recognition results and robot’s actions consists of three stages, respectively, in a special mode and a general mode. The special mode allows users to directly control the manipulator by voice commands. The general mode extends the capabilities of users, allowing them to get additional information in real time. At the first stage, continuous speech recognition is built by using a deep neural network, taking into account the accents and speech speeds of various users. Continuous speech recognition is a real-time voice to text conversion. At the second stage, the correction of the speech recognition result by managing the dialogue based on the theory of finite automata. At the third stage, the actions of the robot are corrected depending on the operating state of the robot and the dialogue management process. In order to realize a natural dialogue between users and robots, the problem is solved in creating a small database of possible dialogues and using various training data. In the experiments, the dialogue system is used to control the KUKA manipulator (KRC4 control) to put the desired block in the specified location, implemented in the Python environment using the RoboDK software. The processes and results of experiments confirming the operability of the interactive robot control system are given. A fairly high accuracy (92 %) and an automatic speech recognition rate close to the rate of natural speech were obtained.

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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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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 recognition is achieved using a normalized cumulative distance. A normalized Dynamic Programming (DP) value is calculated by dividing a cumulative path distance by an optimal integral path length. The path length is calculated iteratively by adding 2 if the warping path is diagonal or by adding 3 if the warping path is horizontal or vertical. Distance may be calculated by measuring a difference between input power and average power. The power difference is weighted by a coefficient (λ) between 0 and 1. A Mahalanobis distance is then weighted by (1-λ) and added to the weighted power difference.

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

Human computer interaction (HCI) is very crucial in our day-to-day activity. Speech is one of the essential and intuitive ways to interact with machines such as Smartphone, which has multiple sensors as microphone, camera, etc. An efficient performance speech recognition system improves interaction between man and machines by making latter more receptive to user needs. Such system has Automatic speech recognition (ASR) engine, which is facing a unique challenge of accuracy in recognition rate. By integrating acoustic signal feature vectors with the visual features, a more robust audiovisual speech recognition engine (AVSR) could be developed for real environmental scenarios. This paper presents past research and development in the field of ASR and AVSR technologies. It describes key technological perspective and admiration of the fundamental progress in ASR and AVSR. The objective of this review is to summarize and compare some of the well-known methods experimented by previous researchers, and to conclude with direction on future research proficiency in HCI system using ASR and AVSR engine.

In this article we present a voice enabled call center whichintegrates our basic and applied research results on Hungarianspeech recognition. Telephone interfaces, data storage and re-trieval modules, and an intelligent dialog descriptor and man-ager module are also parts of the system. To evaluate the ef-ficiency of the recognition and the dialog, a voice enabled callcenter was implemented and tested under real life conditions.This article describes the main modules of the system and com-pares the result of the field tests with that of the laboratory test-ing. 1. Introduction Hungarian speech research has a long tradition at our Labora-tory, there exist some research systems both for speech synthe-sis and for ASR (automatic speech recognition). Our previousASR research results were focused on special problems, likeacoustic model training, language modeling [1], and pronuncia-tion modeling [3]. Recently, large Hungarian speech databaseshave become available, recorded over mobile and fixed tele-phone channels, which encouraged the implementation of di-alog systems that can be used for various services in the field oftelephony. On one hand we had to integrate our previous basicand applied research results while, on the other hand, applyingreal voice driven dialogs initiated a new research area for Hun-garian.In the following section we provide an overview of thecall center realized, going into details about the main compo-nents, especially about the speech recognition engine. Then wedemonstrate our experimental results of the system and comparethem with laboratory test results.

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.

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.