Speech Diarization and ASR with GMM
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914928999268352 |
|---|---|
| author | Sharma, Aayush Kumar Bhavikatti, Vineet Nidawani, Amogh Siddappaji P, Sanath Mishra, Dr Geetishree |
| author_facet | Sharma, Aayush Kumar Bhavikatti, Vineet Nidawani, Amogh Siddappaji P, Sanath Mishra, Dr Geetishree |
| contents | In this research paper, we delve into the topics of Speech Diarization and Automatic Speech Recognition (ASR). Speech diarization involves the separation of individual speakers within an audio stream. By employing the ASR transcript, the diarization process aims to segregate each speaker's utterances, grouping them based on their unique audio characteristics. On the other hand, Automatic Speech Recognition refers to the capability of a machine or program to identify and convert spoken words and phrases into a machine-readable format. In our speech diarization approach, we utilize the Gaussian Mixer Model (GMM) to represent speech segments. The inter-cluster distance is computed based on the GMM parameters, and the distance threshold serves as the stopping criterion. ASR entails the conversion of an unknown speech waveform into a corresponding written transcription. The speech signal is analyzed using synchronized algorithms, taking into account the pitch frequency. Our primary objective typically revolves around developing a model that minimizes the Word Error Rate (WER) metric during speech transcription. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_05637 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Speech Diarization and ASR with GMM Sharma, Aayush Kumar Bhavikatti, Vineet Nidawani, Amogh Siddappaji P, Sanath Mishra, Dr Geetishree Audio and Speech Processing Machine Learning Sound In this research paper, we delve into the topics of Speech Diarization and Automatic Speech Recognition (ASR). Speech diarization involves the separation of individual speakers within an audio stream. By employing the ASR transcript, the diarization process aims to segregate each speaker's utterances, grouping them based on their unique audio characteristics. On the other hand, Automatic Speech Recognition refers to the capability of a machine or program to identify and convert spoken words and phrases into a machine-readable format. In our speech diarization approach, we utilize the Gaussian Mixer Model (GMM) to represent speech segments. The inter-cluster distance is computed based on the GMM parameters, and the distance threshold serves as the stopping criterion. ASR entails the conversion of an unknown speech waveform into a corresponding written transcription. The speech signal is analyzed using synchronized algorithms, taking into account the pitch frequency. Our primary objective typically revolves around developing a model that minimizes the Word Error Rate (WER) metric during speech transcription. |
| title | Speech Diarization and ASR with GMM |
| topic | Audio and Speech Processing Machine Learning Sound |
| url | https://arxiv.org/abs/2307.05637 |