Basic Principles of Speech Recognition Technology
The principles behind speech recognition are generally similar across different systems, even though the specific methods and technologies used may vary. The process involves sending a noise-reduced speech signal into a feature extraction module, processing the speech signal features, and then outputting the recognition results.
Feature extraction plays a critical role in building a speech recognition system, as it greatly influences the accuracy of recognition. The principle is as follows:
Preprocessing
This step filters out secondary information or noise from the original speech, extracts the main audio signal, and converts it into a digital signal. This process is implemented through a microphone.
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Feature Extraction
This step extracts speech feature parameters to form a sequence of feature vectors.
1. Preprocessing
Sound is essentially a wave. Audio files used for speech recognition must be uncompressed formats, such as normal human speech input. The speech environment is complex, with the following main challenges:
Recognition and understanding of natural language. Continuous speech must first be segmented into units such as words or phonemes, and then semantic rules must be established.
Large information content in speech. Speech patterns vary not only between different speakers but also for the same speaker depending on their tone or emotion. A person’s speaking style changes over time.
Ambiguity in speech. Different words may sound similar, which is common in both English and Chinese.
Contextual influence. The pronunciation of letters or words is affected by context, altering stress, tone, volume, and speed.
Environmental noise and interference. Background noise can significantly reduce recognition accuracy.
Therefore, the preprocessing stage must include silence removal, noise reduction, and speech enhancement.
2. Silence Removal
Also known as speech boundary detection or endpoint detection, this technique distinguishes speech segments from non-speech segments in a signal. It accurately identifies the start and end points of speech, enabling subsequent processing to focus only on valid speech segments—improving model precision and recognition accuracy.
In applications, endpoint detection reduces storage or transmission data by separating valid speech from continuous streams. It also simplifies human-computer interaction—for example, automatically ending a recording session once speech stops. Some modern products now use Recurrent Neural Networks (RNNs) for speech endpoint detection.
3. Noise Reduction
Collected audio typically contains background noise. When noise levels are high, they can degrade recognition accuracy and endpoint sensitivity. Thus, noise suppression is essential in front-end speech processing.
The general process involves stabilizing the spectral characteristics of background noise, averaging noise segments using Fourier Transform, and applying compensation to obtain a noise-suppressed signal.
4. Speech Enhancement
The main goal is to eliminate environmental noise interference.
Among various methods, spectral subtraction and its variants—based on short-time spectral estimation—are the most widely used because they are computationally efficient, easy to implement in real time, and provide good enhancement results.
Researchers have also explored using artificial intelligence, Hidden Markov Models (HMMs), neural networks, and particle filters for speech enhancement, though substantial breakthroughs are still pending.
Acoustic Feature Extraction
Humans produce sound through the vocal tract, whose shape—determined by the tongue, teeth, etc.—defines the sound produced. If we can accurately model the shape, we can describe the resulting phonemes precisely.
This shape information appears in the envelope of the power spectrum, and accurately describing this envelope is the main function of acoustic feature extraction.
After preprocessing, the valid speech signal is divided into frames, and for each frame, a multidimensional vector representing its acoustic features is extracted. These vectors serve as the basis for later recognition.
Acoustic Model
After feature extraction, the next step is pattern matching and language processing.
The acoustic model is the foundational and most critical part of a speech recognition system. Its purpose is to compute the distance between speech feature vectors and pronunciation templates efficiently.
The model design depends on linguistic pronunciation characteristics. The size of the model unit (word, syllable, or phoneme) affects training data requirements, recognition accuracy, and system flexibility.
The language model is particularly important for medium and large-vocabulary systems. When classification errors occur, the system uses linguistic, grammatical, and semantic rules for correction. Homophones, for instance, require contextual understanding to determine meaning.
Language models are often based on statistical grammar or rule-based syntax. Grammatical constraints define allowable word connections, reducing the search space and improving recognition efficiency.
Speech recognition is essentially a cognitive process. Just as humans use grammar and semantics to interpret unclear speech, machines must also use such knowledge—though effectively modeling it remains challenging.
Speech Recognition System Types
Small-vocabulary systems: Tens of words.
Medium-vocabulary systems: Hundreds to thousands of words.
Large-vocabulary systems: Thousands to tens of thousands of words.
The vocabulary size directly affects the difficulty of recognition.
Pattern Matching Algorithms
This is a key part of speech recognition systems. Common algorithms include:
Template Matching: e.g., Dynamic Time Warping (DTW)
Statistical Models: e.g., Hidden Markov Model (HMM)
Artificial Neural Networks (ANN)
Hidden Markov Model (HMM):
An HMM is a statistical model derived from Markov chains and is widely used in speech processing. It uses trained parameters to match the probability of observed signals rather than storing fixed templates. Recognition is based on finding the most probable state sequence given the input—making it a highly effective model.
Dynamic Time Warping (DTW):
DTW, based on dynamic programming (DP), is one of the earliest and most effective algorithms for isolated-word recognition. It solves the problem of varying speech duration. DTW requires minimal training, which makes it still valuable for simple recognition tasks.
Artificial Neural Networks (ANN):
ANNs simulate the human brain’s information processing by connecting numerous simple neurons. A speech recognition system based on ANN typically includes neurons, training algorithms, and network structures.
ANNs have high processing speed, adaptability, and self-adjustment capability—continuously tuning parameters and topology during training. This adaptability is one key difference between AI products and traditional internet products.