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Recap and Today's Theme Hello! In the last episode, we discussed speech recognition in noisy environments, covering techniques such as noise reduction and data augmentation to build robust models. Today, we will explore applications of s...

Recap and Today's Theme Hello! In the previous episode, we explored real-time audio processing, learning how to perform speech recognition and synthesis with low latency. Today, we will shift our focus to audio codecs—technologies that c...

Recap and Today's Theme Hello! In the previous episode, we explored audio data augmentation, where we enhanced the diversity of audio datasets using techniques like pitch shifting and time-stretching. Today, we will delve into an excitin...

Recap and Today's Theme Hello! In the previous episode, we explored multimodal learning, which combines data from different modalities (audio, images, text) to build more accurate and robust models. This integration of multiple data type...

Recap and Today's Theme Hello! In the previous episode, we covered speaker recognition, a technique used to identify individual speakers based on voice patterns. Speaker recognition plays a key role in voice assistants and security syste...

Recap and Today's Theme Hello! In the previous episode, we covered keyword spotting (KWS), a technique used to detect specific keywords in real-time, which plays a critical role in voice assistants and smart devices. Today, we will focus...

Recap and Today's Theme Hello! In the previous episode, we discussed emotion recognition from speech, focusing on how audio data can be analyzed to detect the emotions of a speaker. Today, we will explore another important topic: Speaker...

Recap and Today's Theme Hello! In the previous episode, we discussed evaluation metrics for speech recognition systems, such as Word Error Rate (WER), and how these metrics help assess model performance. Today, we will focus on a technol...

Recap and Today's Theme Hello! In the previous episode, we discussed Tacotron 2, a model that generates high-quality speech by converting text into Mel-spectrograms. While Tacotron 2 excels at generating Mel-spectrograms, it still requir...

Recap and Today's Theme Hello! In the previous episode, we explained WaveGlow, a vocoder model that enables high-quality and real-time speech synthesis. By combining WaveGlow with Tacotron 2, we can generate natural-sounding speech effic...

Recap and Today's Theme Hello! In the previous episode, we introduced Text-to-Speech (TTS) technology, which converts text into natural-sounding speech. With advances in deep learning, modern TTS systems, such as Tacotron 2, can generate...

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 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 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 Hidden Markov Models (HMMs), a classical approach to speech recognition. While HMMs decompose speech into phonemes, they face limitations when capturing the complex and...