The Viterbi algorithm is a dynamic programming algorithm widely used in speech recognition decoding to find the most likely sequence of hidden states (e.g., phonemes or words) that generated the observed acoustic signals. In speech recognition, the process involves mapping audio features (observations) to a sequence of words or phonemes (hidden states) using a probabilistic model like a Hidden Markov Model (HMM).
The Viterbi algorithm efficiently computes the optimal path through the HMM states by maximizing the probability of the state sequence given the observations. It avoids the exponential complexity of a brute-force search by storing only the best path to each state at each time step.
Example:
In a speech recognition system, when a user says "hello," the acoustic signal is processed into feature vectors. The HMM models the transition probabilities between phonemes (e.g., /h/, /e/, /l/, /o/) and their emission probabilities for the observed features. The Viterbi algorithm calculates the most probable sequence of phonemes (and ultimately words) that matches the input audio, such as selecting "/h/-/e/-/l/-/l/-/o/" over other possible combinations.
In cloud-based speech recognition services (e.g., Tencent Cloud's ASR service), the Viterbi algorithm is often integrated into the decoding pipeline to ensure fast and accurate transcription by leveraging optimized implementations on scalable infrastructure. Tencent Cloud ASR leverages such algorithms to provide real-time speech-to-text conversion with high accuracy.