Hidden Markov Models (HMMs) have several limitations in speech synthesis, primarily due to their inherent assumptions and architectural constraints.
Assumption of Markov Property: HMMs assume that the current state depends only on the previous state (first-order Markov assumption), which oversimplifies the complex dependencies in speech. Real speech often has long-range dependencies that HMMs cannot capture effectively.
Limited Expressiveness of Acoustic Modeling: HMMs model speech as a sequence of discrete states, forcing the acoustic output (spectral features) to follow a Gaussian Mixture Model (GMM). This struggles to represent the fine-grained variations in speech, leading to unnatural or robotic-sounding output.
Duration Modeling Challenges: HMMs use a separate duration model (e.g., a Gaussian or decision tree) to predict state durations, which is less flexible than directly modeling variable-length phonemes or syllables. This can result in unnatural pauses or stretched sounds.
Difficulty in Modeling Coarticulation: Coarticulation (how phonemes influence each other in continuous speech) is hard to model accurately with HMMs because they treat each state independently, missing subtle transitions between sounds.
Limited Context Handling: While context-dependent HMMs (e.g., triphones) improve performance, they still struggle with broader linguistic context compared to neural approaches like WaveNet or Tacotron.
Example: In a traditional HMM-based TTS system, the word "hello" might be broken into phonemes (e.g., /h/, /ɛ/, /l/, /o/), each modeled as a sequence of states with Gaussian emissions. However, the transitions between phonemes may sound abrupt because HMMs cannot smoothly model the coarticulation effects (e.g., how /h/ and /ɛ/ blend in natural speech).
Alternative in Cloud AI: For more natural speech synthesis, modern systems use neural approaches like Tencent Cloud's Text-to-Speech (TTS) service, which leverages deep learning models (e.g., Tacotron, FastSpeech, or VITS) to generate more expressive and human-like speech by directly modeling raw waveforms or spectrograms without HMM constraints. These models handle long-range dependencies, coarticulation, and prosody much better than HMM-based systems.