Write a note on Speech Recognition and NLP
Answer:-
Speech Recognition:
- Speech recognition is the process of converting spoken language into text.
- The goal is to map an audio signal (e.g., sound frames) into a sequence of words or characters.
- Early Systems:
- Used Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs).
- GMMs modeled the association between acoustic features and phonemes.
- HMMs modeled the sequential nature of speech.
- Advancement with Deep Learning:
- Deep Neural Networks (DNNs) were introduced to directly associate acoustic features with phonemes.
- Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks were later adopted for better performance.
- Introduction of Connectionist Temporal Classification (CTC), which eliminated the need for HMMs and allowed for direct alignment of acoustic features with phonetic information.
- The shift to deep learning resulted in significant improvements in recognition accuracy, including a reduction in error rates.
- Applications:
- Modern speech recognition systems are widely used in devices like smartphones, virtual assistants (e.g., Siri, Google Assistant, Alexa), and transcription services.
Natural Language Processing (NLP):
- NLP is the field of AI focused on enabling computers to understand, interpret, and generate human languages.
- Key Tasks:
- Machine Translation: Translating text from one language to another.
- Sentiment Analysis: Determining the emotional tone of a text.
- Text Summarization: Condensing long text into a shorter version.
- Question Answering: Extracting relevant information from text.
- Challenges:
- Ambiguity in natural language due to context, tone, or phrasing.
- Techniques in NLP:
- Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks help process language sequentially.
- Transformers like BERT and GPT allow for better understanding of context and handling longer sequences of text.
- Word Embeddings (Word2Vec, GloVe) capture the semantic meaning of words as vectors in a high-dimensional space.
- The use of deep learning has drastically improved the ability of machines to process and understand human language.
- Applications:
- Virtual assistants, chatbots, automated customer support systems, and language translation.
- NLP is used across industries such as healthcare, finance, customer service, and entertainment.