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speech to text
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2022-11-14
13:31:04
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speech to text
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Speech-to-text
Introduction + approach
1. Load audio in WMA format, do speech preprocessing and enhancement, including
noise reduction, speech quality enhancement
2. Detect voice activity detection. Process include detecting silent audio parts, and cut
audio at silence and extract only human speeches.
3. We need to separate client and agent. To do so, we will use a similarity-based
approach. In detail, we will store some sample speeches from each agent as
reference speeches. If say now we have a new call log from one agent, we can
retrieve reference speech from this agent, and calculating similarity. Those similar
audio parts belong to the agent, the others belong to the client.
4. After applying the pretrained speech-to-text model, we can get a draft transcript.
Validation Metric
1. We tried to validate the model on 30 call logs. Among the 30 calls, average call duration is
around 5 minutes. We cover 15 agents overall. We can achieve around 71% average
accuracy, 75% median accuracy, and the best accuracy is around 90%.
We also analyze the accuracy per agent base and we can find out our model can result in a
huge difference regarding the transcription accuracy. There are several possible reason: 1.
Our model is a generalized one, it can’t capture some agent’s pronunciation very well 2. If
there is a frequent switch between agent and client, our model will perform much worse
comparing to the call where only the agent is talking. We would like to share you some
demos then.
Any questions here?
Demo – Audio Preparation
First, let’s do some visualizations on audio preprocessing, voice activation and speaker
saparation.
Voice Activity Detection: The green shaded area refers to voice activities, we shall extract
those as human speeches.
Speaker Separation: red dashed line refers to agent, blue dashed line refers to client.
Demo – Good Call
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