Speech Transcription Pipeline: Steps to Prepare Data for ML Models

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Introduction:

Speech Recognition Dataset technology has revolutionised the way we interact with machines, enabling voice-based interactions with various devices and applications. Behind the scenes, building accurate and robust speech recognition models requires a well-prepared dataset. In this blog post, we will explore the steps involved in the speech transcription pipeline and discuss the techniques companies can employ to prepare data for machine learning (ML) models, with a focus on Speech Recognition Datasets.

Data Collection:

The first step in preparing a speech recognition dataset is to collect high-quality speech data. This can be done through various methods:

 

  1. Data Acquisition: Speech data can be collected using audio recording devices or by accessing existing speech databases and repositories. It is crucial to ensure that the collected data is diverse, representing different speakers, accents, languages, and environmental conditions.
  2. Transcription: Once the audio data is collected, it needs to be transcribed into Text Dataset. This can be done manually by human transcribers, who listen to the audio and convert it into written form. Alternatively, automatic speech recognition (ASR) systems can be used for initial Transcription, followed by human review and correction.

Data Cleaning and Preprocessing:

Speech Data often requires cleaning and preprocessing to enhance the quality and compatibility for ML models. Some common techniques include:

 

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  1. Noise Removal: Background noise can significantly impact speech recognition accuracy. Techniques such as spectral subtraction or deep learning-based denoising algorithms can be used to reduce noise and enhance the clarity of the speech signal.
  2. Speech Segmentation: Long audio recordings are often segmented into smaller units, such as sentences or phrases. This allows for better alignment with the corresponding transcriptions and facilitates efficient model training.
  3. Text Normalisation: Speech transcripts may contain non-standard spellings, abbreviations, or acronyms. Normalising the text by expanding abbreviations, correcting spelling errors, and standardising the format ensures consistency and improves the performance of the ML models.

Feature Extraction:

Speech signals need to be converted into a suitable representation for ML models to process. Feature extraction techniques convert raw audio into a set of numerical features that capture relevant information from the speech. Some commonly used features for speech recognition include:

 

Mel Frequency Cepstral Coefficients (MFCCs): MFCCs are widely used features that represent the spectral characteristics of speech. They capture information related to the shape of the vocal tract, which is important for speech recognition.

 

Perceptual Linear Prediction (PLP): PLP features are similar to MFCCs but provide additional robustness against background noise and channel variations. They are particularly useful in noisy environments.

 

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Spectrogram: A spectrogram is a visual representation of the speech signal's frequency content over time. It can be converted into a matrix of spectral features and used as input for ML models.

Dataset Augmentation:

Dataset augmentation techniques can help increase the size and diversity of the speech recognition dataset, leading to improved model generalisation and performance. Some common augmentation techniques include:


  1. Speed Perturbation: Altering the speech signal's speed by slowing it down or speeding it up. This helps in creating variations in speaking rate and improves the model's ability to handle different speech speeds.
  2. Noise Injection: Adding background noise to the clean speech signal simulates real-world scenarios and enhances the model's robustness to noisy environments.
  3. Pitch Shifting: Modifying the pitch or frequency of the speech signal creates variations in speaker characteristics and can help model different voice types.

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Training and Evaluation:

After the data preparation steps, the dataset is ready for training ML models. This typically involves splitting the dataset into training, validation, and testing sets. The training set is used to train the models, the validation set is used to tune hyperparameters and monitor model performance, and the testing set is used to evaluate the final model's accuracy and generalisation.

Conclusion:

Preparing data for speech recognition models involves several critical steps in the transcription pipeline. From data collection and cleaning to feature extraction and augmentation, each step contributes to building a high-quality and diverse speech recognition dataset. By following these steps and employing suitable techniques, companies can train ML models that accurately transcribe speech and enable seamless voice-based interactions. The quality and preparation of the speech recognition dataset are crucial for achieving state-of-the-art performance and delivering effective speech recognition systems. 

HOW GTS. AI can be right Speech Recognition Dataset

Globose Technology Solutions can provide valuable assistance in data generation, augmentation, transcription verification, noise simulation, and labeling for speech recognition datasets. These capabilities can help improve the quality, diversity, and effectiveness of the dataset, leading to enhanced performance of speech recognition models.

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