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# Task Name
African American Vernacular English (AAVE) Speech Recognition
## Task Objective
Mainstream speech recognition systems often perform poorly on non-standard dialects and sociolects,…
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### Introduction
Speaker diarization is the process of partitioning an input audio stream into homogeneous segments according to the speaker's identity. It can enhance the readability of an automatic…
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### Useful links found in the book
1. [TransformersLibrary](https://github.com/huggingface/transformers)
2. [Rnn](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) ![house_generate](https://…
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# Task Name
Dialect Segmentation
## Task Objective
This task aims to identify and differentiate dialects from audio samples from various regions of the United States. Regardless of the countr…
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- [ ] Train or download a KWS model for your hexapod's onboard computer.
- [ ] Respond to keywords using pre-programmed responses or integrate with an AI like ChatGPT for dynamic conversation.
- […
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In ComputeNorm, decision=0 and never updated dynamically to a value that represent the result.
It's defined as zero variable and stays zero all the way till it's printed zero in the result.
How c…
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Many thanks for the contribution,
although the utterance segmentation is not a part of your work (the IEMOCAP emotion dataset is already segmented into utterances), do you have any idea about any too…
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# Task Name
Code-switching refers to the phenomenon where a speaker alternates between two or more languages or dialects within a conversation, sentence, or phrase. This presents a significant chal…
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**Abstract**
Computers can tell us whether we’re happy, sad, angry or any of the several emotions we feel. Computers can understand what we’re saying and answer back. How does all this magic happen? …
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I managed to use PCM 16 bit Voice data, Sampling rate is 44100Hz.
The recognition result always get true even the completely different sound data.
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