Speech/non-speech detection in Malay language spontaneous speech

Izzad, M.; Jamil, Nursuriati; Bakar, Zainab Abu

doi:10.1109/commantel.2013.6482394

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…Speech energy is closely related to the amplitude of the speech (Izzad et al, 2013). Instead of calculating the total energy of each frame, in this research the energy stability of the speech is measured based on the amplitude transition from one frame to another.…”

Section: Proposed Speech Energy Extraction Using Local Maximamentioning

confidence: 99%

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Investigation of Speech Disfluencies Classification on Different Threshold Selection Techniques Using Energy Feature Extraction

Hamzah

Jamil

2019

MJoC

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Filled pause and Elongation are the two types of speech disfluencies that need more suitable acoustical features to be classified correctly since they are always being misclassified. This work concentrates on developing an accurate and robust energy feature extraction for modelling filled pause and elongation by investigating different energy features using local maxima points of the speech energy. Method: In this paper, we extracted peak values from each frame of a voiced signal by implementing different thresholding techniques to classify filled pause and elongation. These energy features are evaluated by using statistical naïve Bayes classifier to see the contribution on the classification processes. Various samples of sustained syllables and filled pauses of spontaneous speech were extracted from Malaysian Parliamentary Debate Database of the year 2008. A naïve Bayes was used as a classifier. We performed F-measure evaluation to investigate the significant differences in mean of filled pause and elongation samples. Results: Results revealed that our proposed LM-E has increase the classification with up to 71% and 75% F-measure for elongation and filled pause. Conclusion: The best achieved accuracies in both filled pause and elongation classification were varied depending on the types of thresholding techniques applied during the local maxima of speech energy extraction. The most contributed thresholding technique is our proposed technique which is by using the adaptive height as the threshold that extracts the local maxima of the speech energy (LM-E).

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Section: Proposed Speech Energy Extraction Using Local Maximamentioning

confidence: 99%

“…The use of energy parameter is customary but not limited in endpoint detection only. It is also beneficial in consonant and vowel detection in (Izzad et al, 2013). However, sum of energy calculated from short time speech frame is unable to detect the energy variation from the consonant and vowel in the elongation.…”

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confidence: 99%

Investigation of Speech Disfluencies Classification on Different Threshold Selection Techniques Using Energy Feature Extraction

Hamzah

Jamil

2019

MJoC

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“…However, those observation was done with a goal to detect turn-taking, thus the major boundaries have role in signaling the completion of turn-taking unit of a speaker only at the end segment of the sentence. Similar approaches for audio sentence presented in [2,3] have been proposed to improve the automatic detection. However, PPh segment is usually defined as a group of words that carries the intended speech meaning.…”

Section: Introductionmentioning

confidence: 99%

Instruction Task for Malay Phrase Boundary Annotation

Hanum¹,

Abdullah²,

Bakar

2018

IJET

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The paper presents a refined instruction task to assist evaluation of prosodic phrase (PPh) boundaries by naive listeners. The results from the perceptual experiments were compared to the boundaries produced by online automatic tagger. The Kappa evaluation shows the average of 85% on inter-rater agreement. More than 60% of the boundaries which are detected by the automatic tagger matched the reference boundaries, showing that the refined instruction task can be used to evaluate perception on phrase boundaries on continuous speech.

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“…Since users' sounds are usually natural and non-planned, they are usually illustrated by repetition, artificial initiate, partial words, discontinue in the core and re-starts, or additional linguistic occurrence such as cough etc.. The speech detection [2] section should be capable to extort away of the verbal communication indication, a speech progression permitting the semantic analyzer to infer the significance of the user's speech. The spontaneous speech recognition is mainly complicated due to representation divergence, for example when a person talks to another person as in conventions or discussions, interview or over telephone conversations.…”

Section: Introductionmentioning

confidence: 99%

Automatic Spontaneous Speech Recognition for Punjabi Language Interview Speech Corpus

Kumar¹,

Singh²

2016

IJEME

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Automatic Speech Recognition presents natural phenomena for the communication among man and machine. The purpose of Speech Recognition speech system is to convert the sequence of sound units in the form of text description. The main objective of the research work is to develop the automatic spontaneous speech model for the Punjabi language. Punjabi is categorized as a constituent of the Indo-Aryan subgroup of the Indo-European family of languages. So far no work has been done in the field of spontaneous Punjabi speech recognition system. In spontaneous speech system, the sounds are usually unprompted and non-designed and are commonly described by repetitions, repairs, false start, partial words and non-planned words, silence gap etc. In this paper, the focus is on the development of the spontaneous speech model for the recognition of the Punjabi language. The GUI for Punjabi speech model also has been created and tested for the live Punjabi interview speech corpus. The recognition accuracy is 98.6% for Punjabi sentences and 98.8% for Punjabi words. The sphinx toolkit and java programming are used to build a spontaneous speech model for Punjabi live speech.

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Speech/non-speech detection in Malay language spontaneous speech

Cited by 8 publications

References 16 publications

Investigation of Speech Disfluencies Classification on Different Threshold Selection Techniques Using Energy Feature Extraction

Investigation of Speech Disfluencies Classification on Different Threshold Selection Techniques Using Energy Feature Extraction

Instruction Task for Malay Phrase Boundary Annotation

Automatic Spontaneous Speech Recognition for Punjabi Language Interview Speech Corpus

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