Tytuł:

Phonetic Segmentation using a Wavelet-based Speech Cepstral Features and Sparse Representation Classifier, Journal of Telecommunications and Information Technology, 2021, nr 4

Autor:

Al-Hassani, Ihsan ; Al-Dakkak, Oumayma ; Assami, Abdlnaser

Temat i słowa kluczowe:

sparse representation classifier ; wavelet packet ; TTS ; ASR ; phonetic segmentation ; Arabic speech corpus ; F1-score

Opis:

Speech segmentation is the process of dividing speech signal into distinct acoustic blocks that could be words, syllables or phonemes. Phonetic segmentation is about finding the exact boundaries for the different phonemes that composes a specific speech signal. This problem is crucial for many applications, i.e. automatic speech recognition (ASR). In this paper we propose a new model-based text independent phonetic segmentation method based on wavelet packet speech parametrization features and using the sparse representation classifier (SRC). Experiments were performed on two datasets, the first is an English one derived from TIMIT corpus, while the second is an Arabic one derived from the Arabic speech corpus. Results showed that the proposed wavelet packet de composition features outperform the MFCC features in speech segmentation task, in terms of both F1-score and R-measure on both datasets. Results also indicate that the SRC gives higher hit rate than the famous k-Nearest Neighbors (k-NN) classifier on TIMIT dataset

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Data wydania:

2021, nr 4

Typ zasobu:

artykuł

Format:

application/pdf

Identyfikator zasobu:

ISSN 1509-4553, on-line: ISSN 1899-8852

Źródło:

Journal of Telecommunications and Information Technology

Język:

ang

Prawa:

Biblioteka Naukowa Instytutu Łączności