Statistical parametric TTS was the dominant approach for Indian language synthesis for most of the 2010s. Formant synthesis and later Hidden Markov Model (HMM)-based systems were what production IVRs ran, what academic researchers published on, and what the available toolkits (Festvox, HTS) were built for. Neural TTS changed the quality ceiling significantly, but the transition was not a clean replacement. This article looks at where we are in 2025 and where the gap between approaches is still surprising.
What statistical parametric TTS actually does
In an HMM-based statistical TTS system, speech is parametrically modeled: the acoustic properties of each phoneme (represented as mel-cepstral coefficients, fundamental frequency contours, and voiced/unvoiced decisions) are modeled with hidden Markov models trained on speaker recordings. To synthesize new text, you run G2P to get a phoneme sequence, then generate the acoustic parameters predicted by the HMM models, then reconstruct the waveform from those parameters using a vocoder (MLSA vocoder in most implementations).
The advantages of this architecture that kept it dominant for Indian languages are real. HMM-based systems are compact: a full TTS system including models and vocoder might be 20-50MB, easily deployable on low-memory embedded systems. Inference is fast and predictable. The systems are interpretable in the sense that you can examine the learned HMM parameters and understand what the model has learned about each phoneme. Training data requirements are manageable: a few hours of clean speaker recordings are sufficient for a usable HMM voice, compared to the much larger datasets neural systems need.
The disadvantages are also real: the parametric vocoding produces a distinctive muffled, buzzy quality at the waveform level that listeners associate with robotic speech. Prosody on long sentences is flat. Naturalness is limited by the parametric representation's inability to capture fine-grained spectral details.
Where neural TTS genuinely surpassed statistical: quality ceiling
The quality jump from HMM-based to neural TTS is not subtle. Neural systems trained on adequate data produce speech that most listeners have difficulty distinguishing from human recordings. The waveform quality improvement from neural vocoders (HiFi-GAN, WaveNet variants) is dramatic: the parametric buzzing disappears, fine spectral texture is preserved, and formant transitions between phonemes sound natural.
For Indic languages specifically, the naturalness improvement on complex phoneme sequences is particularly meaningful. Hindi's conjunct consonant clusters, Tamil's retroflex series, and Malayalam's complex geminate consonants all benefit from the neural model's ability to learn the continuous acoustic realizations of these sounds directly from data, rather than modeling them parametrically with learned means and variances.
We have run informal blind listening comparisons between our neural Hindi model and the best HMM-based Hindi systems (TTS from IIIT Hyderabad and the Festvox Hindi voice). Listeners strongly prefer our neural output. The quality gap is large enough that it is visible even in phone-quality (8kHz) audio, which is the relevant quality for IVR.
Where the gap is still surprising: edge deployments and low-data settings
Neural TTS requires substantially more resources than HMM-based systems. A production-quality neural TTS inference stack requires a few gigabytes of model weights, GPU acceleration for acceptable latency (CPU inference is possible but slow), and more complex software dependencies. For edge deployments on devices with limited memory and compute (industrial IoT, automotive infotainment systems with older processors, feature phones in their more capable variants), HMM-based systems are still the practical choice.
For languages with truly limited training data (under 20-30 hours of clean speech), HMM-based systems can produce usable synthesis while neural systems either fail to train adequately or produce unstable, artifact-prone output. Several regional Indian languages with smaller speaker populations still lack the training data required to train production-quality neural voices. An HMM-based voice, though lower in ceiling quality, is a real and usable product where a neural voice is not yet viable.
This is not a hypothetical concern: several languages we have not yet added to our neural models are actively being served by HMM-based systems in production IVR applications in India. The failure mode of a poor neural model (garbled speech, unstable prosody, occasional severe artifacts) is worse than a consistent-but-robotic HMM model.
Inference speed: mixed picture
HMM-based inference is faster than neural inference on equivalent hardware, and it scales to CPU-only deployments gracefully. Neural inference with GPU acceleration achieves lower latency than HMM on CPU, but the comparison is hardware-dependent. On a modern server GPU, our neural inference beats HMM inference in absolute latency because of our streaming vocoder architecture. On a CPU-only deployment at the edge, HMM inference is faster and more predictable.
Real-time factor (RTF, the ratio of synthesis time to audio duration) for our neural system on GPU is around 0.02 (we generate audio 50x faster than real-time). HMM-based synthesis on equivalent hardware achieves RTF around 0.005. The difference matters for edge deployments where GPU is not available, but for cloud-hosted synthesis the GPU inference advantage dominates.
Looking forward
The trajectory for general-purpose Indic TTS is clearly toward neural dominance as data collection and model compression continue to improve. Quantized and distilled neural models are approaching the memory footprints that allow edge deployment. Within two to three years, the data-constraint argument for HMM systems will weaken as neural training techniques like cross-lingual transfer and semi-supervised learning improve the data efficiency of neural systems.
What will not go away is the edge-hardware argument. If you are synthesizing speech on a device with 256MB RAM and no GPU, HMM-based systems will likely remain the practical answer for years. The voice quality you can achieve on that hardware will improve as model compression research advances, but the physics of running a neural vocoder on minimal compute does not change quickly. For constrained-hardware deployments, the correct answer in 2025 is still "evaluate your hardware constraints before assuming neural TTS is the right choice."