Whither speech production?

Speech intelligibility in cochlear implant (CI) users degrades considerably in listening environments with reverberation and noise. Previous research in automatic speech recognition (ASR) has shown that phoneme-based speech enhancement algorithms improve ASR system performance in reverberant environments as compared to a global model. However, phoneme-specific speech processing has not yet been implemented in CIs. In this paper, we propose a causal deep learning framework for classifying phonemes using features extracted at the time-frequency resolution of a CI processor. We trained and tested long short-term memory networks to classify phonemes and manner of articulation in anechoic and reverberant conditions. The results showed that CI-inspired features provide slightly higher levels of performance than traditional ASR features. To the best of our knowledge, this study is the first to provide a classification framework with the potential to categorize phonetic units in real-time in a CI.

Current Problems in Automatic Speech Recognition

Current research in automatic speech recognition is primarily concerned with the correct evaluation of linguistic information transmitted in the speech signal and with the identification of variations, naturally present in speech. These differences in speech may be due to the individual’s age; gender; or speaking style influenced by his dialect. Undoubtedly, the focus of research in this field is to strengthen further the techniques developed thus far, regarding their reliability and accuracy. The endeavour of this research paper is to primarily concentrate on analysis and modelling of linguistic and paralinguistic information embedded in the speech signal for discovering the similarities and dissimilarities among acoustic characteristics arising out of different dialects. This paper investigates the influence of dialectal variations, by measuring and analysing certain acoustic features such as formant frequencies, pitch, pitch slope, duration and intensity of vowel sounds. For automatic identification of native dialect, these differences are further exploited, given a sample of native speaker’s speech. For the classification of dialect in the spoken utterances support vector machines along with dialect-specific Gaussian mixture models were used. The system performance is compared with human perception of dialects. The proposed study focuses on various dialects of one of the world’s major language; Hindi.

Recently with the wide development of computers, various forms of information exchange between human and computer are discovered. At present, interacting with the computer using speech is one of the active scientific research fields especially for people with disabilities who face variety of barriers to computer use. Such research in Automatic Speech Recognition (ASR) is investigated for different languages because each language has its specific features. This paper presents a speech recognition system for individually spoken word in Tamil language using multilayer feed forward network which falls under the category, "Networks for Classification and Prediction" and has widespread interesting applications and functions related to speech processing. To implement the above system, initially preprocessing is done with the input signal and the speech features being the main part of speech recognition system, are analyzed and extracted via Mel Frequency Cepstral Coefficients (MFCC). These feature vectors are given as the input to the Feed-Forward Neural Network for classifying and recognizing Tamil spoken word. Experiments are done with sample Tamil speech signals and its performance are measured based on Mean square error (MSE). The results indicate that the adopted network with specified parameters have produced the best MSE.KeywordsElliptic FilterMel Frequency Cepstral CoefficientsFeed-forward neural networksTamil Speech RecognitionMean square errorPeak signal to Noise Ratio

Saying that late 20th century automatic speech recognition (ASR) is pattern recognition, is something of a truism, but perhaps one of which the fundamental implications are not always fully appreciated. Essentially, a pattern recognition task boils down to measuring the distance between a physical representation of a new, as yet unknown token, and all elements of a set of preexisting patterns, of course in the same physical representation. On the one hand, the ‘patterns’ that can be recognized are, implicitly or explicitly, separate and invariable entities. For example, the command open in a Windows control application always has the same invariable and unique meaning. On the other hand, the unknown input tokens are continuous signals that typically show a high degree of variability. ASR research has centered around the problem of how to map continuous, variable acoustic representations onto discrete, invariable patterns. In ASR the physical representation of the speech tokens is some kind of dynamic power spectrum, for reasons which date back to the days of Ohm and von Helmholtz, who have shown that the power spectrum explains most of the perceptual phenomena in human speech processing. Since the inception of digital signal processing dynamic spectra are approximated by a sequence of short-time spectra (Rabiner & Schafer 1978). Consequently, the pattern match in ASR is invariably implemented as the accumulation of some distance measure between the acoustic features derived from a sequence of short-time spectra of the input token and the corresponding representation of the active patterns (see Fig. 2.1). Therefore, anything which adds to the variability of the short time spectrum of a speech signal will, as it were by definition, complicate pattern matching, and consequently complicate ASR.

Machine learning techniques have long been the foundations of speech processing. Bayesian classiflcation, decision trees, unsupervised clustering, the EM algorithm, maximum entropy, etc. are all part of existing speech recognition systems. The success of statistical speech recognition has led to the rise of statistical and empirical methods in natural language processing. Indeed, many of the machine learning techniques used in language processing, from statistical part-of-speech tagging to the noisy channel model for machine translation have roots in work conducted in the speech fleld. However, advances in learning theory and algorithmic machine learning approaches in recent years have led to signiflcant changes in the direction and emphasis of the statistical and learning centered research in natural language processing and made a mark on natural language and speech processing. Approaches such as memory based learning, a range of linear classiflers such as Boosting, SVMs and SNoW and others have been successfully applied to a broad range of natural language problems, and these now inspire new research in speech retrieval and recognition. We have seen an increasingly close collaboration between voice and language processing researchers in some of the shared tasks such as spontaneous speech recognition and understanding, voice data information extraction, and machine translation. The purpose of this special issue was to invite speech and language researchers to communicate with each other, and with the machine learning community on the latest machine learning advances in their work. The call for papers was met with great enthusiasm from the speech and natural language community. Thirty six submissions were received; each paper was reviewed by at least three reviewers. Only ten papers were selected re∞ecting not only some of the best work on machine learning in the areas of natural language and spoken language processing but also what we view as a collection of papers that represent current trends in these areas of research both from the perspective of

Combined speech enhancement and auditory modelling for robust distributed speech recognition

This paper discusses and experiments on frame-by-frame speech signal processing and recognition for Field Programmable Gate Array (FPGA) devices. The system proposes applications including a voice conversion system that needs signal processing and speech recognition for each frame because it requires real-time processing at each frame. Owing to the processing speed, the authors propose algorithms for FPGA as a hardware processor for Voice Activity Detection (VAD) and speech recognition decoder. However, resources for FPGA devices as gate circuits are minimal, therefore, the algorithms need to be customized in order to implement the FPGA. The algorithms are customized for VAD using a 2nd-order autocorrelation function, and for speech recognition using Euclidian distance. These methods implement an FPGA emulator that demonstrates VAD of speech and noise sections and a speech recognition experiment for discriminating Japanese vowels.

Speech recognition can be defined as the process of converting voice signals into the ranks of the word, by applying a specific algorithm that is implemented in a computer program. The research of speech recognition in Indonesia is relatively limited. This paper has studied methods of feature extraction which is the best among the Linear Predictive Coding (LPC) and Mel Frequency Cepstral Coefficients (MFCC) for speech recognition in Indonesian language. This is important because the method can produce a high accuracy for a particular language does not necessarily produce the same accuracy for other languages, considering every language has different characteristics. Thus this research hopefully can help further accelerate the use of automatic speech recognition for Indonesian language. There are two main processes in speech recognition, feature extraction and recognition. The method used for comparison feature extraction in this study is the LPC and MFCC, while the method of recognition using Hidden Markov Model (HMM). The test results showed that the LPC method is better than MFCC in Indonesian language speech recognition.

For the past two decades, research in speech recognition has been intensively carried out worldwide, spurred on by advances in signal processing, algorithms, architectures, and hardware. Speech recognition systems have been developed for a wide variety of applications, ranging from small vocabulary keyword recognition over dial-up telephone lines, to medium size vocabulary voice interactive command and control systems on personal computers, to large vocabulary speech dictation, spontaneous speech understanding, and limited-domain speech translation. In this paper we review some of the key advances in several areas of automatic speech recognition. We also illustrate, by examples, how these key advances can be used for continuous speech recognition of Mandarin. Finally we elaborate the requirements in designing successful real-world applications and address technical challenges that need to be harnessed in order to reach the ultimate goal of providing an easy-to-use, natural, and flexible voice interface between people and machines.

Disabled individuals can realize many benefits from automatic speech recognition (ASR). To date, most ASR research has focused on normal speech. However, many individuals with physical disabilities also exhibit speech disorders. While limited research has been conducted focusing on dysarthric speech recognition, the preliminary results indicate that additional study is necessary. Recently, increasing attention has been given to multimodal speech recognition schemes that utilize multiple input sources-most commonly audio and video. This multimodal approach has been applied to normal speech with demonstrated effectiveness. Through studying the effect of audio and visual information in a human perception experiment, this study attempts to discover whether such an approach would be useful for dysarthric speech recognition. Results of a closed-vocabulary perception test are presented. In this test, 15 normal-hearing viewers were presented with video-tapes of three dysarthric speakers (with cerebral palsy) speaking a series of one-syllable nonsense words. These words differed only in the initial consonant. The words were presented in both audio-only and audio-visual modes. Perception rates in both modes were measured. The results are analyzed and compared to other studies of visual speech perception and dysarthric speech articulation.

Voice recognition has become more and more popular in various systems and applications. To further promote Macau tourism worldwide, a mobile Macau tourism APP is being developing that supports voice control to facilitate Portuguese users. Consequently, this paper is about the research and implementation of an Automatic Speech Recognition (ASR) engine for Portuguese language. In this research, three well-known open-source ASR platforms were evaluated and compared. The complete ASR development procedure using Kaldi platform is discussed. Due to the limitation of collected voice data, a novel few-shot learning and transfer learning is implemented in this project. The final model achieved a stable 95.25% accuracy which is good enough for production use. The novel technics implemented in this research can be used for ASR trainings with limited training data and can be extended to a wide range of applications in the future.KeywordsASRPortuguese voice recognitionFew-shot learningTransfer learning

Discriminative training techniques have been shown to consistently outperform the maximum likelihood (ML) paradigm for acoustic model training in automatic speech recognition (ASR). Consequently, today's discriminative training methods are fundamental components of state-of-the-art systems and are a major line of research in speech recognition. This article gives a comprehensive overview of discriminative training methods for acoustic model training in the context of ASR. The article covers all related aspects of discriminative training for speech recognition, i.e., specific training criteria and their relation, statistical modeling, different parameter optimization approaches, efficient implementation of discriminative training, and a performance overview.

For the past two decades, research in speech recognition has been intensively carried out worldwide, spurred on by advances in signal processing, algorithms, architectures, and hardware. Speech recognition systems have been developed for a wide variety of applications, ranging from small vocabulary keyword recognition over dial-up telephone lines, to medium size vocabulary voice interactive command and control systems on personal computers, to large vocabulary speech dictation, spontaneous speech understanding, and limited-domain speech translation. In this chapter we review some of the key advances in several areas of automatic speech recognition. We also briefly discuss the requirements in designing successful real-world applications and address technical challenges that need to be faced in order to reach the ultimate goal of providing an easy-to-use, natural, and flexible voice interface between people and machines.

Emotionally coloured speech recognition is a key technology toward achieving human-like spoken dialog systems. However, despite rapid progress in automatic speech recognition (ASR) and emotion research, much less work has examined ASR systems that recognize the verbal content of emotionally coloured speech. Approaches that exist in emotional speech recognition mostly involve adapting standard ASR models to include information about prosody and emotion. In this study, instead of adapting a model to handle emotional speech, we focus on feature transformation methods to solve the mismatch and improve the ASR performance. In this way, we can train the model with emotionally coloured speech without any explicit emotional annotation. We investigate the use of two different deep bottleneck network structures: deep neural networks (DNNs) and convolutional neural networks (CNNs). We hypothesize that the trained bottleneck features may be able to extract essential information that represents the verbal content while abstracting away from superficial differences caused by emotional variance. We also try various combinations of these two bottleneck features with feature-space speaker adaptation. Experiments using Japanese and English emotional speech data reveal that both varieties of bottleneck features and feature-space speaker adaptation successfully improve the emotional speech recognition performance.