Speaker Identification using Speech Recognition
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Abstract (arXiv)
The audio data is increasing day by day throughout the globe with the increase of telephonic conversations, video conferences and voice messages. This research provides a mechanism for identifying a speaker in an audio file, based on the human voice biometric features like pitch, amplitude, frequency etc. We proposed an unsupervised learning model where the model can learn speech representation with limited dataset. Librispeech dataset was used in this research and we were able to achieve word error rate of 1.8.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 12 references that are in the map. First posted on arXiv 29 May 2022.
Builds on
8 of 12 references.
References · 8
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
- 2020Unsupervised Cross-lingual Representation Learning for Speech Recognitioncited by 501
- 2019vq-wav2vec: Self-Supervised Learning of Discrete Speech Representationscited by 340
- 2021Unsupervised Speech Recognitioncited by 148
- 2020Improved Noisy Student Training for Automatic Speech Recognitioncited by 141
- 2020Multi-task self-supervised learning for Robust Speech Recognitioncited by 130
- 2019Unsupervised speech representation learning using WaveNet autoencoderscited by 90
- 2019Improving Transformer-based Speech Recognition Using Unsupervised Pre-trainingcited by 30
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