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Dialect identification is the task of classifying speech on the basis of dialect, which comes under the Automatic Language Identification problem. In this work a neuro fuzzy classifier is used to identify dialect of speech from vowel sound. Vowel sounds occur in an acoustic speech signal more frequently and with higher energy. Therefore, prosodic feature of vowel sounds can be used to search dialect...
The speaker change information in speech is due to both vocal tract and excitation source information. In this work, the excitation source information is extracted by computing cepstral features from the zero frequency filtered speech (ZFFS) signal. The vocal tract system information is extracted by computing cepstral features from the speech signal. The speaker change evidences obtained from these...
In this work, a Recurrent Neural Network (RNN) is trained using cepstral features and a set of difference cepstral feature (DCF) vectors on a frame to frame basis. The DCF vector is formulated to capture the temporal patterns of fricative sounds or phonemes of Assamese language. A hybrid algorithm is developed to recognize these fricative phonemes from certain words containing them. To preserve the...
A novel technique for speech signal reconstruction using Empirical Mode Decomposition (EMD) of speech signal in noisy condition is described in this paper. EMD is applied for finding the glottal source signal of speech signals. After getting the source information, vocal tract filter response is determined and the original speech signal is reconstructed with the help of EMD with and without prior...
The phonetic engine is a system that performs speech signal to symbol transformation. This work describes some issues in the development of an Assamese Phonetic Engine (PE). International phonetic alphabet (IPA) is used as the phonetic unit to transcribe the speech database collected in three different modes, namely, reading, lecture and conversation modes. Only reading mode data is used for training...
Vowels are the phonemes with greatest intensity and low frequencies. Assamese, which is considered as the lingua-franca of the entire north-east India, has eight vowel phonemes namely /i/, /e/, /ε/, /a/, /ɒ/, /ɔ/, /o/ and /u/. A Recurrent Neural Network (RNN) based algorithm is described in this paper for the recognition of the vowel sounds from Assamese speech. The feature vector is generated by...
Fricatives are the major group of speech sounds bearing distinct acoustical and phonetical characteristics and provides a wide range of application possibilities in the field of speech and speaker recognition. Assamese, which is a widely spoken language in the north eastern part of India, has four distinct fricative sounds called /s/, /z/, /x/ and /h /. In this paper, a Recurrent Neural Network (RNN)...
In this paper, a novel technique for speech signal reconstruction is described using Empirical Mode Decomposition (EMD) of speech signal. EMD is applied for finding the glottal source signal of speech signals. After getting the source information, vocal tract filter response is determined and the original speech signal is reconstructed. The experimental result derived establishes the effectiveness...
This paper presents a neural model of speaker identification using the vowel sound segmented out from words spoken by a speaker. Vowel sounds occur in a speech more frequently and with higher energy. Therefore, situations where acoustic information is noise corrupted vowel sounds can be used to extract different amounts of speaker discriminative information. The model explained here uses a neural...
Initial phoneme is used in spoken word recognition models. These are used to activate words starting with that phoneme in spoken word recognition models. Such investigations are critical for classification of initial phoneme into a phonetic group. A work is described in this paper using an artificial neural network (ANN) based approach to recognize initial consonant phonemes of Assamese words. A self...
Phonemes are the smallest distinguishable unit of speech signal. Formant frequency of a phoneme, the most fundamental concept in speech processing, differentiate one phoneme from another. Range of formant frequency of a particular phoneme can be used as a priori knowledge in various speech processing application. This paper describes a work done for estimating the formant frequencies of all consonant...
Phonemes are the smallest distinguishable unit of speech signal. Segmentation of phoneme from its word counterpart is a fundamental and crucial part in speech processing since initial phoneme is used to activate words starting with that phoneme. This work describes an Artificial Neural Network (ANN) based algorithm developed for segmentation and classification of consonant phoneme of Assamese language...
The quality and details captured in speech corpus directly affects the precision of performance in an Automatic Speech Recognition (ASR) system. The current work proposes a platform for speech corpus generation using an adaptive LMS filter and LPC Cepstrum, as a part of an Artificial Neural Network (ANN) based Speech Recognition System which is exclusively designed to recognize isolated numerals of...
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