Speaker Identification using Speech Recognition
This addresses the problem of identifying speakers in increasing audio data for applications like telephony and conferencing, but it appears incremental as it builds on existing biometric and speech recognition methods.
The research tackled speaker identification in audio files by using human voice biometric features, achieving a word error rate of 1.8% on the Librispeech dataset.
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.