Across every track, you will

- Drive AI research and innovation in Speech/Audio

AI

- Translate research questions into measurable hypotheses; design rigorous experiments and ablations, analyze model behavior and failure modes, reproduce and build on relevant papers

- Take research from prototype to production-grade, deployable systems

- Define appropriate objective and subjective evaluation metrics, and run benchmarking, error analysis, and statistical comparisons to know when a model is actually better – not just different

- Collaborate cross-functionally with engineering, product, and DSP/embedded teams

- Bring strong problem-solving ability – debug, iterate, and optimize models under real-world constraints

- Stay current with SOTA literature and bring current techniques into the team, publish or patent where appropriate

Track 1: Automatic Speech Recognition (ASR)

- Work on end-to-end ASR architectures (Transformer, Conformer, RNN-T) for streaming speech recognition

- Work with speech foundation models (Whisper-style architectures) and self-supervised models (HuBERT, wav2vec 2.0, WavLM) for representation learning and fine-tuning

- Own the end-to-end ASR pipeline – preprocessing, feature extraction, acoustic modeling, and decoding/language modeling

- Research streaming and causal architectures under strict latency constraints

- Improve robustness under noisy, multi-accent, and low-resource conditions – starting with Indian-English / regional Indian accents, with a roadmap to scale to international accents and languages





- Research accent-invariant / accent-normalized representations – removing accent information from learned embeddings to improve generalization across accents and geographies

- Study representation quality and robustness across speakers, accents, recording conditions, and domains

- Evaluated on: WER, CER, RTF, latency, memory

- Preferred background: prior work on CTC/RNN-T, self-supervised pretraining, or Whisper finetuning

Track 2: Text-to-Speech (TTS)

- Work on flow-matching / diffusion-based TTS systems (in the spirit of CosyVoice, ZipVoice, VITS, and similar SOTA architectures) for real-time, low-compute synthesis

- Experience with duration-controlled, time-aligned TTS using forced alignment (e.g., MFA) and Monotonic Alignment Search (MAS) for multi-speaker settings

- Research controllable accent adaptation using speaker, accent, linguistic, prosodic, and/or style representations – including decoder-side conditioning and representation-level approaches – starting with Indian-English and US-English accent pairs and scaling to international accents

- Research controllable duration, timing, rhythm, F0, and prosody modelling for natural accent rendering.

- Improve naturalness, prosody, and voice

-cloning quality

- Optimize inference for real-time, streaming synthesis on constrained hardware

- Evaluated on: MOS, speaker similarity, intelligibility, prosody, RTF

- Preferred background: prior work with neural vocoders, diffusion/flow-matching TTS, or zeroshot voice cloning

📌 Speech Language Specialist (Hyderabad)
🏢 Zilo AI
📍 Hyderabad