Natural Language Processing in Microsoft Azure- Speech Recognition

Notes from my learning journey in Coursera inspired from Coach (GenAI tool)

Speech recognition agent with futuristic design generated by AI (copilot)

Natural Language Processing (NLP) is used in different industries including:

1. Healthcare: to analyze medical records and extract relevant information, such as patient symptoms, diagnoses, and treatment plans. This helps healthcare providers make more informed decisions and improve patient care.
2. Customer Service: NLP is used in chatbots and virtual assistants to understand and respond to customer inquiries. It can analyze customer messages, identify their intent, and provide appropriate responses, improving customer satisfaction and reducing response times.
3. Finance: to analyze news articles, social media posts, and financial reports to identify trends, sentiment, and market insights. This helps financial institutions make data-driven investment decisions and manage risks.
4. E-commerce: to analyze customer reviews and feedback to understand customer preferences, sentiment, and product recommendations. This helps e-commerce platforms personalize product recommendations and improve customer satisfaction.
5. Legal: to analyze legal documents, contracts, and case law to extract relevant information, identify patterns, and assist in legal research. This helps legal professionals save time and improve the accuracy of their work.

Speech recognition is a service which converts spoken words into data that can be processed through the following steps:

1. Audio Input: The speech recognition system takes in audio input, which can be in the form of a recorded voice in an audio file or live audio from a microphone.
2. Acoustic Model: The audio signal is analyzed using an acoustic model. The acoustic model converts the audio signal into phonemes, which are representations of specific sounds in a language. The model maps the audio patterns to phonemes, which are the basic units of speech.
3. Language Model: The language model maps the phonemes to words. It uses a statistical algorithm that predicts the most probable sequence of words based on the phonemes detected by the acoustic model. The language model helps in determining the intended words from the recognized phonemes.
4. Text Representation: The recognized words are typically converted into text, which can be used for various purposes such as providing closed captions for videos, creating transcripts of phone calls, or generating automated note dictation.

By analyzing the audio patterns and mapping them to phonemes and words, speech recognition systems convert spoken words into data that can be further processed and utilized in various applications.

More details about how speech recognition service converts audio input into phonemes by using an acoustic model.

1. Audio Analysis: The audio input, which can be in the form of a recorded voice or live audio, is analyzed by the speech recognition system. The system breaks down the audio signal into smaller segments, such as frames or chunks, to process them effectively.
2. Feature Extraction: From each segment of the audio, the system extracts various acoustic features. These features can include characteristics like the frequency, intensity, and duration of the sound. These features help in capturing the unique properties of different phonemes.
3. Acoustic Model Training: The acoustic model is trained using a large dataset of labeled audio samples. These samples contain both the audio recordings and their corresponding phonetic transcriptions. The model learns to associate the extracted acoustic features with the phonemes present in the training data.
4. Pattern Matching: During the recognition phase, the acoustic model compares the extracted features from the audio input with the patterns it has learned during training. It calculates the likelihood of each phoneme being present in the audio based on the similarity between the features and the patterns.
5. Phoneme Sequence: The acoustic model generates a sequence of phonemes that best matches the audio input based on the calculated likelihoods. This sequence represents the recognized phonetic representation of the spoken words.

For live demo about those services check my Youtube channel AI4Innovation

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