Effects of Syllable Language Model on Distinctive Phonetic Features (DPFs) based Phoneme Recognition Performance

The robustness of speech classification and recognition systems can be improved by the adoption of language distinctive phonetic feature (DPF) elements that can increase the effective characterization of a speech signal. This paper presents the results of applying Hidden Markov Models (HMMs) that perform Arabic phoneme recognition in conjunction with the inclusion and classification of their DPF element classes. The research focuses on classifying Modern Standard Arabic (MSA) phonemes within isolated words without a language context. HMM-based phoneme recognition is tested using 8, 16, and 32 HMM Gaussian mixture models. The monophone configuration is designed with consideration of 2-gram language model to evaluate the inherent performance of the system. The overall correct rates for classifying DPF element classes for the three versions of HMM systems are 83.29% 88.96%, and 92.70% for 8, 16, and 32 HMM Gaussian mixture model systems, respectively.

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Phoneme recognition based on distinctive phonetic features (DPFs) incorporating a syllable based language model

2009 12th International Conference on Computers and Information Technology ◽

10.1109/iccit.2009.5407123 ◽

2009 ◽

Author(s):

Mohammad Nurul Huda ◽

Manoj Banik ◽

Ghulam Muhammad ◽

Bernd J. Kroger

Keyword(s):

Language Model ◽

Phoneme Recognition ◽

Phonetic Features

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Optimizing Arabic Speech Distinctive Phonetic Features and Phoneme Recognition Using Genetic Algorithm

IEEE Access ◽

10.1109/access.2020.3034762 ◽

2020 ◽

Vol 8 ◽

pp. 200395-200411

Author(s):

Ahmed B. Ibrahim ◽

Yasser Mohammad Seddiq ◽

Ali Hamid Meftah ◽

Mansour Alghamdi ◽

Sid-Ahmed Selouani ◽

...

Keyword(s):

Genetic Algorithm ◽

Phoneme Recognition ◽

Phonetic Features

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Speech Feature Evaluation for Bangla Automatic Speech Recognition

Technical Challenges and Design Issues in Bangla Language Processing ◽

10.4018/978-1-4666-3970-6.ch009 ◽

2013 ◽

pp. 169-208 ◽

Cited By ~ 1

Author(s):

Mohammed Rokibul Alam Kotwal ◽

Foyzul Hassan ◽

Mohammad Nurul Huda

Keyword(s):

Speech Recognition ◽

Automatic Speech Recognition ◽

Recognition Performance ◽

Dynamic Parameters ◽

Mel Frequency Cepstral Coefficients ◽

Phoneme Recognition ◽

Hybrid Features ◽

Feature Evaluation ◽

Speech Feature ◽

Speech Features

This chapter presents Bangla (widely known as Bengali) Automatic Speech Recognition (ASR) techniques by evaluating the different speech features, such as Mel Frequency Cepstral Coefficients (MFCCs), Local Features (LFs), phoneme probabilities extracted by time delay artificial neural networks of different architectures. Moreover, canonicalization of speech features is also performed for Gender-Independent (GI) ASR. In the canonicalization process, the authors have designed three classifiers by male, female, and GI speakers, and extracted the output probabilities from these classifiers for measuring the maximum. The maximization of output probabilities for each speech file provides higher correctness and accuracies for GI speech recognition. Besides, dynamic parameters (velocity and acceleration coefficients) are also used in the experiments for obtaining higher accuracy in phoneme recognition. From the experiments, it is also shown that dynamic parameters with hybrid features also increase the phoneme recognition performance in a certain extent. These parameters not only increase the accuracy of the ASR system, but also reduce the computation complexity of Hidden Markov Model (HMM)-based classifiers with fewer mixture components.

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Improvement of Lecture Speech Recognition by Using Unsupervised Adaptation

E-Activity and Intelligent Web Construction - Advances in Web Technologies and Engineering ◽

10.4018/978-1-61520-871-5.ch016 ◽

2011 ◽

pp. 189-202

Author(s):

Tetsuo Kosaka ◽

Takashi Kusama ◽

Masaharu Kato ◽

Masaki Kohda

Keyword(s):

Large Scale ◽

Markov Models ◽

Recognition Performance ◽

Language Model ◽

Spontaneous Speech ◽

Part Of Speech ◽

Speech Database ◽

Adaptation Procedure ◽

Lm Adaptation ◽

Optimal Approach

The aim of this work is to improve the recognition performance of spontaneous speech. In order to achieve the purpose, the authors of this chapter propose new approaches of unsupervised adaptation for spontaneous speech and evaluate the methods by using diagonal-covariance and full-covariance hidden Markov models. In the adaptation procedure, both methods of language model (LM) adaptation and acoustic model (AM) adaptation are used iteratively. Several combination methods are tested to find the optimal approach. In the LM adaptation, a word trigram model and a part-of-speech (POS) trigram model are combined to build a more task-specific LM. In addition, the authors propose an unsupervised speaker adaptation technique based on adaptation data weighting. The weighting is performed depending on POS class. In Japan, a large-scale spontaneous speech database “Corpus of Spontaneous Japanese (CSJ)” has been used as the common evaluation database for spontaneous speech and the authors used it for their recognition experiments. From the results, the proposed methods demonstrated a significant advantage in that task.

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WAVELET SUB-BAND BASED TEMPORAL FEATURES FOR ROBUST HINDI PHONEME RECOGNITION

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691310003845 ◽

2010 ◽

Vol 08 (06) ◽

pp. 847-859 ◽

Cited By ~ 19

Author(s):

O. FAROOQ ◽

S. DATTA ◽

M. C. SHROTRIYA

Keyword(s):

Recognition Performance ◽

Wavelet Packet ◽

Phoneme Recognition ◽

Linear Discriminant ◽

Temporal Features ◽

Admissible Wavelet ◽

Different Types ◽

Noisy Background ◽

Frequency Band Energy ◽

Feature Extraction Technique

This paper proposes the use of wavelet transform-based feature extraction technique for Hindi speech recognition application. The new proposed features take into account temporal as well as frequency band energy variations for the task of Hindi phoneme recognition. The recognition performance achieved by the proposed features is compared with the standard MFCC and 24-band admissible wavelet packet-based features using a linear discriminant function based classifier. To evaluate robustness of these features, the NOISEX database is used to add different types of noise into phonemes to achieve signal-to-noise ratios in the range of 20 dB to -5 dB. The recognition results show that under noisy background the proposed technique always achieves a better performance over MFCC-based features.

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Effect of Articulatory Trajectories on Phoneme Recognition Performance

2010 International Conference on Intelligent Computing and Cognitive Informatics ◽

10.1109/icicci.2010.37 ◽

2010 ◽

Author(s):

Mohamm Huda ◽

Ghulam Muhammad ◽

Mohammad Mahedi Hasan ◽

Sharif Mohammad Musfiqur Rahman ◽

Foyzul Hassan ◽

...

Keyword(s):

Recognition Performance ◽

Phoneme Recognition

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Boosting of Speech Recognition Performance by Language Model Adaptation

2007 IEEE Aerospace Conference ◽

10.1109/aero.2007.352980 ◽

2007 ◽

Author(s):

Filipp Korkmazsky ◽

Oliver Jojic ◽

Bageshree Shevade

Keyword(s):

Speech Recognition ◽

Recognition Performance ◽

Language Model ◽

Model Adaptation ◽

Language Model Adaptation

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Concept Type Prediction and Responsive Adaptation in a Dialogue System

Dialogue & Discourse ◽

10.5087/dad.2012.101 ◽

2012 ◽

Vol 3 (1) ◽

pp. 1-31 ◽

Cited By ~ 1

Author(s):

Svetlana Stoyanchev ◽

Amanda J. Stent

Keyword(s):

Speech Recognition ◽

Recognition Performance ◽

Language Model ◽

Dialogue System ◽

Dialog Systems ◽

Language Model Adaptation ◽

Dialog System ◽

Spoken Dialog System ◽

Type Classification ◽

Concept Type

Responsive adaptation in spoken dialog systems involves a change in dialog system behavior in response to a user or a dialog situation. In this paper we address responsive adaptation in the automatic speech recognition (ASR) module of a spoken dialog system. We hypothesize that information about the content of a user utterance may help improve speech recognition for the utterance. We use a two-step process to test this hypothesis: first, we automatically predict the task-relevant concept types likely to be present in a user utterance using features from the dialog context and from the output of first-pass ASR of the utterance; and then, we adapt the ASR's language model to the predicted content of the user's utterance and run a second pass of ASR. We show that: (1) it is possible to achieve high accuracy in determining presence or absence of particular concept types in a post-confirmation utterance; and (2) 2-pass speech recognition with concept type classification and language model adaptation can lead to improved speech recognition performance for post-confirmation utterances.

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A Chinese Small Vocabulary Offline Speech Recognition System Based on Pocketsphinx in Android Platform

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.623.267 ◽

2014 ◽

Vol 623 ◽

pp. 267-273

Author(s):

Xin Fei Liu ◽

Hui Zhou

Keyword(s):

Speech Recognition ◽

Cell Phone ◽

Recognition Performance ◽

Language Model ◽

Recognition System ◽

Speech Recognition System ◽

Development Environment ◽

Online Tool ◽

Android Development ◽

The Voice

This paper describes a Chinese small-vocabulary offline speech recognition system based on PocketSphinx which acoustic models are regenerated by improving the existing models of Sphinx and language model is generated by LMTool online tool. And then build an offline speech recognition system which could run on the Android smartphone in Android development environment in Linux system. The experiment results show that the system used for recognizing the voice commands for cell phone has good recognition performance.

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