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.

本文探讨语音分离和自动语音识别两个主题。我们使用高斯混合模型和距离阈值作为停止标准来实现语音分离，在自动语音识别方面通过分析语音信号并考虑声音频率达到最小化词错误率指标的目标。