Speech Summarization for Tamil Language

NithyaKalyani, A.; Jothilakshmi, S.

doi:10.1016/b978-0-12-818130-0.00007-6

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…MFCC based comparative study of speech recognition techniques was conducted by authors who found that MFCC with HMM gave recognition accuracy of 85 percent and with deep neural networks the score was 82.2 percent [56]. Computation of MFCCs includes a conversion of the Fourier coefficients to Mel-scale [57]. Mel-scale are the most popular variant used today, even if there is no theoretical reason that the Mel-scale is superior to the other scales [58].…”

Section: C1 Mfccmentioning

confidence: 99%

Big Data Classification for the Analysis MEL Scale Features Using KNN Parameterization

Osadchyy¹,

Skuratovskii

2020

International Journal of Circuits, Systems and Signal Processing

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The role of human speech is intensified by the emotion it conveys. The parameterization of the vector obtained from the sentence divided into the containing emotional-informational part and the informational part is effectively applied. There are several characteristics and features of speech that differentiate it among utterances, i.e. various prosodic features like pitch, timbre, loudness and vocal tone which categorize speech into several emotions. They were supplemented by us with a new classification feature of speech, which consists in dividing a sentence into an emotionally loaded part of the sentence and a part that carries only informational load. Therefore, the sample speech is changed when it is subjected to various emotional environments. As the identification of the speaker’s emotional states can be done based on the Mel scale, MFCC is one such variant to study the emotional aspects of a speaker’s utterances. In this work, we implement a model to identify several emotional states from MFCC for two datasets, classify emotions for them on the basis of MFCC features and give the comparison of both. Overall, this work implements the classification model based on dataset minimization that is done by taking the mean of features for the improvement of the classification accuracy rate in different machine learning algorithms.

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Section: C1 Mfccmentioning

confidence: 99%

Big Data Classification for the Analysis MEL Scale Features Using KNN Parameterization

Osadchyy¹,

Skuratovskii

2020

International Journal of Circuits, Systems and Signal Processing

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Analysis of the MFC Singuliarities of Speech Signals Using Big Data Methods

Skuratovskii

Osadchyy²

2021

Lecture Notes in Networks and Systems

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Analyzing the Effects of Transcription Errors on Summary Generation of Bengali Spoken Documents

Chowdhury,

Sarkar,

Nath

et al. 2024

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Automatic speech recognition (ASR) has become an indispensable part of the AI domain, with various speech technologies reliant on it. The quality of speech recognition depends on the amount of annotated data used to train an ASR system, among other factors. For a low-resourced language, this is a severe constraint and thus ASR quality is often poor. Humans can read through text containing ASR-errors, provided the context of the sentence is preserved. Yet in cases of transcripts generated by ASR systems of low-resource languages, multiple important words are misrecognized and the context is mostly lost; discerning such a text becomes nearly impossible. This paper analyzes the types of transcription errors that occur while generating ASR transcripts of spoken documents in Bengali, an under-resourced language predominantly spoken in India and Bangladesh. The transcripts of the Bengali spoken document are generated using the ASR of Google Cloud Speech. The paper also explores if there is an effect of such transcription errors in generating speech summaries of these spoken documents. Summarization is carried out extractively; sentences are selected from the ASR-generated text of the spoken document. Speech summaries are created by aggregating the speech-segments from the original speech of the selected sentences. Subjective evaluation shows the ‘readability’ of the spoken summaries are not degraded by ASR errors, but the quality is affected due to the reliance on intermediate text-summary containing transcription errors.

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Speech Summarization for Tamil Language

Cited by 4 publications

References 19 publications

Big Data Classification for the Analysis MEL Scale Features Using KNN Parameterization

Big Data Classification for the Analysis MEL Scale Features Using KNN Parameterization

Analysis of the MFC Singuliarities of Speech Signals Using Big Data Methods

Analyzing the Effects of Transcription Errors on Summary Generation of Bengali Spoken Documents

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