First automatic fongbe continuous speech recognition system: Development of acoustic models and language models

By F. Laleye, Laurent Besacier, E. C. Ezin, C. Motamed. 2016

20 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computational Linguistics & Semantics, Speech & Audio, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. James R. Glass
2. Yoshua Bengio
3. Hung-yi Lee
4. Mohit Bansal
5. Dong Yu
6. Quoc V. Le
7. Graham Neubig
8. Shinji Watanabe

In Computational Linguistics & Semantics

1. Phil Blunsom
2. Chris Dyer
3. Kazuya Kawakami
4. Karen Livescu
5. Aäron van den Oord
6. Luyu Wang
7. Yoav Goldberg

In Speech & Audio

1. Abdel-rahman Mohamed
2. Hung-yi Lee
3. Shinji Watanabe
4. Tara N. Sainath
5. Jinyu Li
6. Shang-Wen Li
7. Karen Livescu
8. Wei-Ning Hsu

In Signal Processing

1. Dong Yu
2. Hung-yi Lee
3. Shinji Watanabe
4. Jinyu Li
5. Tara N. Sainath
6. Abdel-rahman Mohamed
7. James R. Glass
8. Karen Livescu

Citations in the corpus

• Learning Robust and Multilingual Speech Representations (2020, positive): “order to evaluate the transferability of the representations, we use speech recognition datasets in 4 African languages collected by the ALFFA project,3 Amharic (Tachbelie et al., 2014), Fongbe (A. A Laleye et al., 2016), Swahili (Gelas et al., 2012), Wolof (Gauthier et al., 2016), for evaluation. T”
• Self-Supervised Speech Representation Learning: A Review (2022, background): “The ALFFA project3 collects speech of African languages to promote the development of speech technologies in Africa, and [123] leverages four African languages collected in the project for evaluation: Amharic [241], Fongbe [242], Swahili [243], and”