Automatic disordered sound repetition recognition in continuous speech using CWT and kohonen network

Studies the problem of phonetic modeling for continuous Mandarin speech recognition by providing a systematic performance comparison for systems based on following primitive speech units: syllables, demi-syllables (initial and final), context-independent phones, left-or-right context-dependent phones (diphones) and left-and-right context-dependent phones (triphones). In our speaker-dependent continuous speech recognition experiments, a generalized triphone system has achieved the best performance of all. Our best system is in contrast to most other Mandarin speech recognition systems, which have been based on demi-syllable units.

The authors present an NN-IIR (neural network/infinite impulse response filter) system for phoneme recognition in continuous speech based on the idea of modeling the recognition process by state evolution and interpretation equations. This work gives a solution to temporal information representation in phoneme recognition using neural networks and recursive filters, yielding better recognition results for continuous speech. This recognition system has two promising properties, i.e., capabilities for dealing with sequential properties and for interpreting speech signals by means of a training process. It was shown experimentally that the NN-IIR network obtained good performance for continuous speech recognition. Preliminary experiments with limited training data indicate that the NN-IIR provides good discrimination power for plosives, which are highly context-dependent.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Continuous speech is far more natural and efficient than isolated speech for communication. However, for current state-of-the-art automatic speech recognition systems, isolated speech recognition (ISR) is far more accurate than continuous speech recognition (CSR). It is common practice in the speech research community to build CSR systems using only CS data. However, slowing of the speaking rate is a natural reaction for a user faced with the high error rates of current CSR systems. Ironically, CSR systems typically have a much higher word error rate when speakers slow down since the acoustic models are usually derived exclusively from continuous speech corpora. In this paper, we summarize our efforts to improve the robustness of our speaker-independent CSR system against speaking styles, without suffering a recognition accuracy penalty. In particular the multi-style trained system described in this paper attains a 7.0% word error rate for a test set consisting of both isolated and continuous speech, in contrast to the 10.9% word error rate achieved by the same system trained only on continuous speech.

The choice of basic modeling unit in building acoustic model for a continuous Mandarin speech recognition task is a very important issue [1]. Unlike traditional phoneme or Initial/Finals (IFs) units based acoustic modeling methods, which usually suffer from the limitations of less accuracy in modeling intrasyllable variations and long scale temporal dependencies, in this paper, a practicable syllable based approach is presented. In contrast with IFs, syllable can implicitly model the intrasyllable variations in good accuracy. Also, by carefully choosing context modeling schemes and parameter tying methods, syllable based acoustic model can capture longer temporal variations while keeping the complexity of model well controlled. Meanwhile, considering the data unbalanced problem, multiple sized unit model based approaches are also implemented in this research. The experiment result shows the acoustic model based on the presented syllable based approach is effective in improving the performance of the Chinese continuous speech recognition.

Combined speech enhancement and auditory modelling for robust distributed speech recognition

A connectionist structure for phoneme recognition in continuous speech is described. This has two main parts. The first is a sound subunit classifier in the form of a three-layer back propagation network which classifies speech subunits from frames of spectral speech data. This is followed by a sequence classifier in the form of a network of neural like-units which classifies phonemes from input sequences of subunits by their occurrence and duration. Results are given for a 15-phoneme subset of British English, for a single speaker. These include the difficult syllable initial and final stop consonants, fricatives, vowels, and diphthongs. The overall recognition accuracy achieved is 87%. >

In this paper, we propose a system of a tree evolutionary recurrent self-organizing models. Inherited from the Growing Hierarchical Self-Organizing Map GHSOM. The proposed GHSOM variants are characterized by a hierarchical model, composed of independent RSOMs (recurrent Self-Organizing Map). The case study of the proposed system is phoneme recognition in continuous speech and speaker independent context. GHSOM variants serve az tools for developing intel- ligent systems and proposed artificial intelligence application.

For extra-large vocabulary continuous speech recognition a language model that describes permissible phrases is needed. In the paper, the results of experiments on extra-large vocabulary (above 100 K words) continuous speech recognition, with usage of n-gram models, are presented. A quantitative comparison of recognition accuracy of words, symbols, and phonemes depending on n-gram model, with n value varying from 0 till 3, was made.

Inherent features of the Bangla (widely used as Bengali) language like long and short vowels and many instances of allophones make it difficult to build a continuous speech recognizer for the language. Stress and accent vary in spoken Bangla language from region to region. But in formal read Bangla speech, stress and accents are ignored. There are three approaches to continuous speech recognition (CSR) based on the sub-word unit viz. word, phoneme and syllable. Pronunciation of words and sentences are strictly governed by set of linguistic rules. Many attempts have been made to build continuous speech recognizers for Bangla for small and restricted tasks. However, medium and large vocabulary CSR for Bangla is relatively new and not explored. In this paper, the authors have attempted for extracting local features (LFs) from a Bangla input speech for tri-phone based automatic speech recognition (ASR) method. The method comprises two stages, where the first stage extracts LFs from input speech and the final stage generates word strings based on trip hone hidden Markov models (HMMs). The objective of this research is to build a medium vocabulary trip hone based continuous speech recognizer for Bangla language by extracting LFs over mel frequency cepstral coefficients (MFCCs). In this experimentation using Bangla speech corpus prepared by us, the recognizer provides higher word accuracy, word correct rate as well as sentence correct rate for trained and tested sentences with fewer mixture components in HMMs.

In a study presented at the fall 2010 meeting of the Acoustical Society of America (Zahorian etal., “Time/frequency resolution of acoustic features for automatic speech recognition”), we demonstrated that spectral/temporal evolution features which emphasize temporal aspects of acoustic features, with relatively low spectral resolution, are effective for phonetic recognition in continuous speech. These features are computed using discrete cosine transform coefficients for spectral information from 8 ms frames and discrete cosine series coefficients (DCSCs) for their temporal evolution, over overlapping intervals (blocks) longer than 200 ms. These features are presented as an alternative to mel-frequency cepstral coefficients, and their delta terms, for automatic speech recognition (ASR). In the present work, it is shown that these features are even more effective for ASR, using a non-symmetric time window which is tilted toward the beginning of each block when computing DCSCs. This non-symmetry can be implemented by combining two Gaussian windows with different standard deviations. This work also supports the hypothesis that the left context is somewhat more informative to phonetic identity than is the right context. Experimental results for automatic phone recognition are given for various conditions using the TIMIT and NTIMIT databases.

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.

In this paper, the improvements in the recently implemented Kannada speech recognition system is demonstrated in detail. The Kannada automatic speech recognition (ASR) system consists of ASR models which are created by using Kaldi, IVRS call flow and weather and agricultural commodity prices information databases. The task specific speech data used in the recently developed spoken dialogue system had high level of different background noises. The different types of noises present in collected speech data had an adverse effect on the on line and off line speech recognition performances. Therefore, to improve the speech recognition accuracy in Kannada ASR system, a noise reduction algorithm is developed which is a fusion of spectral subtraction with voice activity detection (SS-VAD) and minimum mean square error spectrum power estimator based on zero crossing (MMSE-SPZC) estimator. The noise elimination algorithm is added in the system before the feature extraction part. An alternative ASR models are created using subspace Gaussian mixture models (SGMM) and deep neural network (DNN) modeling techniques. The experimental results show that, the fusion of noise elimination technique and SGMM/DNN based modeling gives a better relative improvement of 7.68% accuracy compared to the recently developed GMM-HMM based ASR system. The least word error rate (WER) acoustic models could be used in spoken dialogue system. The developed spoken query system is tested from Karnataka farmers under uncontrolled environment.

The main objective of this paper is to explore the effectiveness of perceptual features for performing isolated digits and continuous speech recognition. The proposed perceptual features are captured and code book indices are extracted. Expectation maximization algorithm is used to generate HMM models for the speeches. Speech recognition system is evaluated on clean test speeches and the experimental results reveal the performance of the proposed algorithm in recognizing isolated digits and continuous speeches based on maximum log likelihood value between test features and HMM models for each speech. Performance of these features is tested on speeches randomly chosen from “TI Digits_1”, “TI Digits_2” and “TIMIT” databases. This algorithm is tested for VQ and combination of VQ and HMM speech modeling techniques. Perceptual linear predictive cepstrum yields the accuracy of 86% and 93% for speaker independent isolated digit recognition using VQ and combination of VQ & HMM speech models respectively. This feature also gives 99% and 100% accuracy for speaker independent continuous speech recognition by using VQ and the combination of VQ & HMM speech modeling techniques.

Automatic lip-reading (ALR) is a challenging task and a significant amount of research has been devoted to this topic in recent years. However, continuous Russian speech recognition still remains a not well-investigated area. In this paper, we present the results of Russian visual speech recognition (VSR) system using pixel-based and advanced geometry-based features. A HAVRUS video database, comprising of 4000 utterances of continuous Russian speech, collected from 20 speakers, is used in this study. Pixel-based features (principal component analysis-based or PCA) and geometry-based features (active appearance model-based or AAM) were used for the feature representation, and a Gaussian mixture hidden Markov models (HMM) were used for classification. Our evaluation experiments show a significant improvement (up to 9%) in recognition accuracy by using proposed geometry-based features when compared to pixel-based PCA features. The combined VSR is planned for future studies to augment the performance of audio-based automatic speech recognition systems in human–robot interfaces.

We like to conversate with other people using both sounds and visuals, as our perception of speech is bimodal. Essentially echoing the same speech structure, we manage to integrate the two modalities and often understand the message better than with the eyes closed. In this work we would like to learn more about the visual nature of speech, coined lip-reading, and to make use of it towards better automatic speech recognition systems. Recent developments in the Machine Learning area, together with the release of suitable audio-visual datasets aimed at large vocabulary continuous speech recognition, have led to a renewal of the lip-reading topic, and allow us to address the recurring question of how to better integrate visual and acoustic speech.