The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform

Jean-paul Haton - One of the best experts on this subject based on the ideXlab platform.

  • Automatic Speech Recognition: A Review
    Enterprise Information Systems V, 2005
    Co-Authors: Jean-paul Haton
    Abstract:

    Automatic Speech Recognition (ASR) has been extensively studied during the past few decades. Most of present systems are based on statistical modeling, both at the acoustic and linguistic levels, not only for Recognition, but also for understanding. Speech Recognition in adverse conditions has recently received increased attention since noise resistance has become one of the major bottlenecks for practical use of Speech recognizers. After briefly recalling the basic principles of statistical approaches to ASR (especially in a Bayesian framework), we present the types of solutions that have been proposed so far in order to obtain good performance in real life conditions.

  • Neural networks for Automatic Speech Recognition: a review
    Speech Processing Recognition and Artificial Neural Networks, 1999
    Co-Authors: Jean-paul Haton
    Abstract:

    Most present Automatic Speech Recognition systems are based on stochastic models, especially hidden Markov models (HMMs). However, during the past ten years, several projects have been directed toward the use of a new class of models: the connectionist artificial neural networks (ANNs).

  • Fundamentals of Automatic Speech Recognition
    Robustness in Automatic Speech Recognition, 1996
    Co-Authors: Jean-claude Junqua, Jean-paul Haton
    Abstract:

    After summarizing the difficulties encountered in Automatic Speech Recognition (ASR), we briefly describe the main approaches to ASR and present a historical review. We proceed by introducing popular distance measures used to evaluate the differences between extracted parameters. Then, we focus on the main pattern Recognition approaches, namely dynamic programming algorithms, stochastic modeling, and neural networks. We conclude this chapter by reviewing speaker-dependent and speaker-independent Recognition along with common discriminant methods used to improve ASR of confusable words.

Ivo Ipšić - One of the best experts on this subject based on the ideXlab platform.

  • Croatian Large Vocabulary Automatic Speech Recognition
    Automatika -- Journal for Control Measurement Electronics Computing and Communications, 2011
    Co-Authors: Sanda Martinčić-ipšić, Miran Pobar, Ivo Ipšić
    Abstract:

    This paper presents procedures used for development of a Croatian large vocabulary Automatic Speech Recognition system (LVASR). The proposed acoustic model is based on context-dependent triphone hidden Markov models and Croatian phonetic rules. Different acoustic and language models, developed using a large collection of Croatian Speech, are discussed and compared. The paper proposes the best feature vectors and acoustic modeling procedures using which lowest word error rates for Croatian Speech are achieved. In addition, Croatian language modeling procedures are evaluated and adopted for speaker independent spontaneous Speech Recognition. Presented experiments and results show that the proposed approach for Automatic Speech Recognition using context-dependent acoustic modeling based on Croatian phonetic rules and a parameter tying procedure can be used for efficient Croatian large vocabulary Speech Recognition with word error rates below 5%.

Sanda Martinčić-ipšić - One of the best experts on this subject based on the ideXlab platform.

  • Croatian Large Vocabulary Automatic Speech Recognition
    Automatika -- Journal for Control Measurement Electronics Computing and Communications, 2011
    Co-Authors: Sanda Martinčić-ipšić, Miran Pobar, Ivo Ipšić
    Abstract:

    This paper presents procedures used for development of a Croatian large vocabulary Automatic Speech Recognition system (LVASR). The proposed acoustic model is based on context-dependent triphone hidden Markov models and Croatian phonetic rules. Different acoustic and language models, developed using a large collection of Croatian Speech, are discussed and compared. The paper proposes the best feature vectors and acoustic modeling procedures using which lowest word error rates for Croatian Speech are achieved. In addition, Croatian language modeling procedures are evaluated and adopted for speaker independent spontaneous Speech Recognition. Presented experiments and results show that the proposed approach for Automatic Speech Recognition using context-dependent acoustic modeling based on Croatian phonetic rules and a parameter tying procedure can be used for efficient Croatian large vocabulary Speech Recognition with word error rates below 5%.

Denis Jouvet - One of the best experts on this subject based on the ideXlab platform.

  • About vocabulary adaptation for Automatic Speech Recognition of video data
    2017
    Co-Authors: Denis Jouvet, Mohamed Amine Menacer, Odile Mella, Dominique Fohr, David Langlois, Kamel Smaïli
    Abstract:

    This paper discusses the adaptation of vocabularies for Automatic Speech Recognition. The context is the transcriptions of videos in French, English and Arabic. Baseline Automatic Speech Recognition systems have been developed using available data. However, the available text data, including the GigaWord corpora from LDC, are getting quite old with respect to recent videos that are to be transcribed. The paper presents the collection of recent textual data from internet for updating the Speech Recognition vocabularies and training the language models, as well as the elaboration of development data sets necessary for the vocabulary selection process. The paper also compares the coverage of the training data collected from internet, and of the GigaWord data, with finite size vocabularies made of the most frequent words. Finally, the paper presents and discusses the amount of out-of-vocabulary word occurrences, before and after update of the vocabularies, for the three languages.

  • An enhanced Automatic Speech Recognition system for Arabic
    2017
    Co-Authors: Mohamed Amine Menacer, Odile Mella, Dominique Fohr, Denis Jouvet, David Langlois, Kamel Smaïli
    Abstract:

    Automatic Speech Recognition for Arabic is a very challenging task. Despite all the classical techniques for Automatic Speech Recognition (ASR), which can be effi-ciently applied to Arabic Speech recogni-tion, it is essential to take into consider-ation the language specificities to improve the system performance. In this article, we focus on Modern Standard Arabic (MSA) Speech Recognition. We introduce the chal-lenges related to Arabic language, namely the complex morphology nature of the lan-guage and the absence of the short vowels in written text, which leads to several po-tential vowelization for each graphemes, which is often conflicting. We develop an ASR system for MSA by using Kaldi toolkit. Several acoustic and language models are trained. We obtain a Word Er-ror Rate (WER) of 14.42 for the baseline system and 12.2 relative improvement by rescoring the lattice and by rewriting the output with the right hamoza above or below Alif.

  • Automatic Speech Recognition and Intrinsic Speech Variation
    Icassp, 2006
    Co-Authors: M. Benzeguiba, L. Fissore, Steéphane Dupont, Thalia Erbes, Denis Jouvet, O. Deroo, R. Mori, Pietro Laface, Alfred Mertins, Christoph . Ris
    Abstract:

    This paper briefly reviews state of the art related to the topic of Speech variability sources in Automatic Speech Recognition systems. It focuses on some variations within the Speech signal that make the ASR task difficult. The variations detailed in the paper are intrinsic to the Speech and affect the different levels of the ASR processing chain. For different sources of Speech variation, the paper summarizes the current knowledge and highlights specific feature extraction or modeling weaknesses and current trends.

Navdeep Singh - One of the best experts on this subject based on the ideXlab platform.

  • A Comprehensive View of Automatic Speech Recognition System - A Systematic Literature Review
    2019 International Conference on Automation Computational and Technology Management (ICACTM), 2019
    Co-Authors: Yogesh Kumar, Navdeep Singh
    Abstract:

    Humans have always attempted to correspond with objects in a natural language. Communications have been the essential feature of human life, a powerful tool for sharing and building the information that is passed from generation to generation. Among Speech processing problems, Automatic Speech Recognition mechanisms of converting the recorded Speech signals into the text are one of the most challenging tasks. The signals are typically processed in a digital representation, so Speech processing can be observed as a particular case of digital signal processing. The overall performance of an Automatic Speech Recognition system greatly depends upon the acoustic modeling. Hence, building a precise and robust acoustic model holds the key to a suitable Recognition performance. People have used different methods for automated Speech Recognition system. For recognizing the Speech people always choose the English language in the majority of the research and implementation but very less work is done in other languages. Our analysis presents the study of the different Speech Recognition systems present in Indian and foreign languages in the systematic review of Speech Recognition paper. This paper gives the review of different aspects related to Automatic Speech Recognition. We have elaborated the recent advancement in the Speech Recognition system, robust method for the development of an Automatic Speech Recognition system and application of Automatic Speech Recognition system in different fields.

  • Literature Review on Automatic Speech Recognition
    International Journal of Computer Applications, 2012
    Co-Authors: Wiqas Ghai, Navdeep Singh
    Abstract:

    Automatic Speech Recognition, which was considered to be a concept of science fiction and which has been hit by number of performance degrading factors, is now an important part of information and communication technology. Improvements in the fundamental approaches and development of new approaches by researchers have lead to the advancement of ASRs which were just responding to a set of sounds to sophisticated ASRs which responds to fluently spoken natural language. Using artificial neural networks (ANNs), mathematical models of the low-level circuits in the human brain, to improve Speech-Recognition performance, through a model known as the ANN-Hidden Markov Model (ANN- HMM) have shown promise for large-vocabulary Speech Recognition systems. Achieving higher Recognition accuracy, low Word error rate, developing Speech corpus depending upon the nature of language and addressing the issues of sources of variability through approaches like Missing Data Techniques & Convolutive Non-Negative Matrix Factorization, are the major considerations for developing an efficient ASR. In this paper, an effort has been made to highlight the progress made so far for ASRs of different languages and the technological perspective of Automatic Speech Recognition in countries like China, Russian, Portuguese, Spain, Saudi Arab, Vietnam, Japan, UK, Sri- Lanka, Philippines, Algeria and India.

  • Analysis of Automatic Speech Recognition Systems for Indo-Aryan Languages: Punjabi A Case Study
    2012
    Co-Authors: Wiqas Ghai, Navdeep Singh
    Abstract:

     Abstract— Punjabi, Hindi, Marathi, Gujarati, Sindhi, Bengali, Nepali, Sinhala, Oriya, Assamese, Urdu are prominent members of the family of Indo-Aryan languages. These languages are mainly spoken in India, Pakistan, Bangladesh, Nepal, Sri Lanka and Maldive Islands. All these languages contain huge diversity of phonetic content. In the last two decades, few researchers have worked for the development of Automatic Speech Recognition Systems for most of these languages in such a way that development of this technology can reach at par with the research work which has been done and is being done for the different languages in the rest of the world. Punjabi is the 10 th most widely spoken language in the world for which no considerable work has been done in this area of Automatic Speech Recognition. Being a member of Indo-Aryan languages family and a language rich in literature, Punjabi language deserves attention in this highly growing field of Automatic Speech Recognition. In this paper, the efforts made by various researchers to develop Automatic Speech Recognition systems for most of the Indo-Aryan languages, have been analysed and then their applicability to Punjabi language has been discussed so that a concrete work can be initiated for Punjabi language.