音声認識と音声処理（301～330）– 音声データの処理と音声認識の基本を学びます。 –

Recap and Today's Theme Hello! In the previous episode, we discussed Wav2Vec, a self-supervised learning model used to extract features from audio data, which significantly improves the accuracy of speech recognition systems. Today, we w...

Recap and Today's Theme Hello! In the previous episode, we discussed DeepSpeech, a speech recognition model that utilizes CTC (Connectionist Temporal Classification) and deep learning to convert audio data into text in an end-to-end fash...

Recap and Today's Theme Hello! In the previous episode, we discussed Connectionist Temporal Classification (CTC), a method that solves the problem of differing input and output sequence lengths in speech recognition. CTC allows deep lear...

Recap and Today's Theme Hello! In the previous episode, we discussed Hidden Markov Models (HMMs), a classical approach to speech recognition. While HMMs decompose speech into phonemes, they face limitations when capturing the complex and...

Recap and Today's Theme Hello! In the previous episode, we explored the basics of speech recognition, understanding how audio is converted into text and how different components like acoustic models and language models work together. Tod...

Recap and Today's Theme Hello! In the previous episode, we covered audio preprocessing techniques such as normalization, filtering, and adjusting the sampling rate. These steps are essential for improving the quality of audio data and en...

Recap and Today's Theme Hello! In the previous episode, we discussed noise reduction, learning various techniques to remove noise from audio data to improve its quality. Noise reduction is an essential step in enhancing the overall clari...

Recap and Today's Theme Hello! In the previous episode, we discussed Mel-Frequency Cepstral Coefficients (MFCC), a crucial tool for extracting features from audio data used in speech recognition and acoustic analysis. Today, we’ll focus ...

Recap and Today's Theme Hello! In the previous episode, we explored spectrograms, learning how to break down audio signals into frequency components and display them over time. Spectrograms are a crucial tool for visually understanding t...

Recap and Today's Theme Hello! In the previous episode, we explored waveform visualization, learning how to display audio signals over time as changes in amplitude. By visualizing waveforms, we could understand the intensity and certain ...

Recap and Today's Theme Hello! In the previous episode, we explored LibROSA, a powerful Python library for audio processing. We learned how to load, play, and extract features from audio files easily. Now, it's time to move on to a more ...

Recap and Today's Theme Hello! In the previous episode, we covered the basics of audio data, discussing key concepts such as sampling rate and bit depth. Understanding these fundamentals allows you to handle audio data properly and manag...

Recap and Today's Theme Hello! In the previous episode, we discussed audio processing and covered the foundational technologies of digital audio, including speech recognition and speech synthesis. We learned about how audio is digitized ...

Recap and Today's Theme Hello! In the previous episode, we summarized Chapter 10 and conducted a knowledge check to review and deepen our understanding. Now, we’re moving into Chapter 11, where we will learn about speech recognition and ...