A review of Yor&amp;#x00F9;b&amp;#x00E1; Automatic Speech Recognition

Yusof, Shahrul Azmi Mohd; Atanda, A F; Hariharan, M.

doi:10.1109/icsengt.2013.6650178

Cited by 4 publications

(2 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mohamed and Aly [9] presented 'a deep learning emotional recognition model for Arabic speech dialogues'. Here, 'audio representations -based wav2vec2.0 and HuBERT' were used [10]. The performance of the model overcome the previous known results…”

Section: Introductionmentioning

confidence: 86%

Development of Yoruba Dialects Classification Model for Automatic Speech Recognition Systems Using KNN

O.K,

A. A,

A.I.O.

2024

COMIEN

View full text Add to dashboard Cite

This research presents, the development of Yoruba dialects classification Model for automatic speech recognition systems (ASRs) using K-Nearest Neighbor (K-NN). Research had revealed that ASRs perform better with correct dialects classification. Therefore, a non-parametric (i.e K-NN) model was developed and implemented on a Matlab 2021 platform to classify three (3) dialects (Ijebu, Ibadan and Ondo) from Ogun, Oyo and Ondo states respectively of Nigeria. The dialects were recorded at different environments, data sizes and at “opus file” format. They were later converted to “.wav” using the EZ CD Audio Converter Software. The Program4Pc Video Converter Pro was used to trim the converted audio waveforms to the same size and converted them to image signals suitable for model training, validation and testing. The results showed that the developed K-NN Classifier worked with an average performance accuracy of 91.11% and Recall {Sensitivity) of 86.67%. These results indicated that the model can be used to classify dialects of the same language hence, can help to improve the performance of robust ASR systems. However, for further improvement, better Classifiers that can handle large volumes of data should be employer.

show abstract

Section: Introductionmentioning

confidence: 86%

Development of Yoruba Dialects Classification Model for Automatic Speech Recognition Systems Using KNN

O.K,

A. A,

A.I.O.

2024

COMIEN

View full text Add to dashboard Cite

show abstract

“…Another language of West Africa (Yoruba) spoken mainly in Nigeria, in Benin and neighboring countries has been also investigated for an ASR system. [4] provides a brief review of research progress on Yorúbà Automatic Speech Recognition.…”

Section: Introductionmentioning

confidence: 99%

First Automatic Fongbe Continuous Speech Recognition System: Development of Acoustic Models and Language Models

Laleye¹,

Besacier²,

Ezin³

et al. 2016

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-This paper reports our efforts toward an ASR system for a new under-resourced language (Fongbe). The aim of this work is to build acoustic models and language models for continuous speech decoding in Fongbe. The problem encountered with Fongbe (an African language spoken especially in Benin, Togo, and Nigeria) is that it does not have any language resources for an ASR system. As part of this work, we have first collected Fongbe text and speech corpora that are described in the following sections. Acoustic modeling has been worked out at a graphemic level and language modeling has provided two language models for performance comparison purposes. We also performed a vowel simplification by removing tones diacritics in order to investigate their impact on the language models.

show abstract

Explicit Tone Transcription Improves ASR Performance in Extremely Low-Resource Languages: A Case Study in Bribri

Coto-Solano¹

2021

Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas

View full text Add to dashboard Cite

Linguistic tone is transcribed for input into ASR systems in numerous ways. This paper shows a systematic test of several transcription styles, using as an example the Chibchan language Bribri, an extremely low-resource language from Costa Rica. The most successful models separate the tone from the vowel, so that the ASR algorithms learn tone patterns independently. These models showed improvements ranging from 4% to 25% in character error rate (CER), and between 3% and 23% in word error rate (WER). This is true for both traditional GMM/HMM and end-to-end CTC algorithms. This paper also presents the first attempt to train ASR models for Bribri. The best performing models had a CER of 33% and a WER of 50%. Despite the disadvantage of using hand-engineered representations, these models were trained on only 68 minutes of data, and therefore show the potential of ASR to generate further training materials and aid in the documentation and revitalization of the language. ResumenTranscribir el tono de forma explícita mejora el rendimiento del reconocimiento de voz en idiomas extremadamente bajos en recursos: Un estudio de caso en bribri. Hay numerosas maneras de transcribir el tono lingüístico a la hora de proveer los datos de entrenamiento a los sistemas de reconocimiento de voz. Este artículo presenta un experimento sistemático de varias formas de transcripción usando como ejemplo la lengua chibcha bribri, una lengua de Costa Rica extremadamente baja en recursos. Los modelos más exitosos fueron aquellos en que el tono aparece separado de la vocal de tal forma que los algoritmos pudieran aprender los patrones tonales por separado. Estos modelos mostraron mejoras de entre 4% y 26% en el error de caracteres (CER), y de entre 3% y 25% en el error de palabras (WER). Esto se observó tanto en los algoritmos GMM/HMM como en los algoritmos CTC de secuenciaa-secuencia. Este artículo también presenta el primer intento de entrenar modelos de reconocimiento de voz en bribri. Los mejores modelos tuvieron un CER de 33% y un WER de 50%. A pesar de la desventaja de usar representaciones diseñadas a mano, estos modelos se entrenaron con solo 68 minutos de datos y muestran el potencial para generar más materiales de entrenamiento, así como de ayudar con la documentación y revitalización de la lengua.

show abstract

A review of Yorùbá Automatic Speech Recognition

Cited by 4 publications

References 39 publications

Development of Yoruba Dialects Classification Model for Automatic Speech Recognition Systems Using KNN

Development of Yoruba Dialects Classification Model for Automatic Speech Recognition Systems Using KNN

First Automatic Fongbe Continuous Speech Recognition System: Development of Acoustic Models and Language Models

Explicit Tone Transcription Improves ASR Performance in Extremely Low-Resource Languages: A Case Study in Bribri

Contact Info

Product

Resources

About