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https://github.com/farzadforuozanfar/speech-recognition
I recorded 10 voices with the same words from myself and compared them with another 10 words from another person. I was able to find a threshold level that acknowledges and recognizes my own voice.
https://github.com/farzadforuozanfar/speech-recognition
distance dtw dtw-algorithm jupyter-notebook python3 speech-processing speech-recognition speech-to-text
Last synced: 28 days ago
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I recorded 10 voices with the same words from myself and compared them with another 10 words from another person. I was able to find a threshold level that acknowledges and recognizes my own voice.
- Host: GitHub
- URL: https://github.com/farzadforuozanfar/speech-recognition
- Owner: FarzadForuozanfar
- License: mit
- Created: 2022-04-25T09:21:00.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-04-26T18:48:09.000Z (over 2 years ago)
- Last Synced: 2024-05-11T05:49:27.159Z (8 months ago)
- Topics: distance, dtw, dtw-algorithm, jupyter-notebook, python3, speech-processing, speech-recognition, speech-to-text
- Language: Jupyter Notebook
- Homepage:
- Size: 7.48 MB
- Stars: 25
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## **Project Description:**
In this project, I first converted my recorded sounds to text using `librosa`, `dtw`, and `speech_recognition` libraries, which can also be a way of recognizing speech, so-called text-dependency. A more reliable way is done using the **coefficient** of `Mel` or `Capstral`.
## DTW (Dynamic time warping) :
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In time series analysis, dynamic time warping (DTW) is an algorithm for measuring similarity between two temporal sequences, which may vary in speed. For instance, similarities in walking could be detected using DTW, even if one person was walking faster than the other, or if there were accelerations and decelerations during the course of an observation. DTW has been applied to temporal sequences of video, audio, and graphics data — indeed, any data that can be turned into a linear sequence can be analyzed with DTW. A well-known application has been automatic speech recognition, to cope with different speaking speeds. Other applications include speaker recognition and online signature recognition. It can also be used in partial shape matching applications.
In general, DTW is a method that calculates an optimal match between two given sequences (e.g. time series) with certain restriction and rules:
- `Every index` from the first sequence must be matched with one or more indices from the other sequence, and vice versa
- `The first index` from the first sequence must be matched with the first index from the other sequence (but it does not have to be its only match)
- `The last index` from the first sequence must be matched with the last index from the other sequence (but it does not have to be its only match)
- The mapping of the indices from the first sequence to indices from the other sequence must be monotonically increasing, and vice versa, i.e. if ***j>i*** are indices from the first sequence, then there must not be two indices ***l>k*** in the other sequence, such that index ***i*** is matched with index ***l*** and index ***j*** is matched with index ***k***, and vice versa
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## My voice signals using the `matplotlib.pyplot` chart drawing function:
## Another person voice signals using the `matplotlib.pyplot` chart drawing function:
## Compare two(me & another person) audio signals using dtw.plot():
## Calculate distance between my voices to each other with dtw.distance() :
## Calculate distance between my voices and another person voice to each other with dtw.distance() :
