Speech-to-Speech Translation from Kazakh into Turkic Languages: Pretrained Speech Encoders, Turkic Speech Units, and Lessons from Negative Results

Speech-to-speech translation (S2ST) among Turkic languages is rarely studied due to the scarcity of parallel speech corpora and the complex morphology. In this work, we develop low-resource S2ST systems translating from Kazakh to Turkish, Tatar, and Uzbek, and examine which architectural choices proved ineffective and why. Three architectures were trained on a synthetic parallel speech corpus: (i) a scratch-trained, morphology-aware Conformer encoder with a morpheme-level connectionist temporal classification (CTC) loss, a vector-quantized (VQ) codebook, and a semantic anchor; (ii) a pretrained Whisper encoder with a text decoder conditioned on the target language by feature-wise linear modulation (FiLM); and (iii) a text-mediated two-pass model that predicts Turkic speech units from the decoder states and converts them to speech with a Turkic unit vocoder. Text output was scored with BLEU and speech output with ASR-BLEU, that is, BLEU on automatic transcripts. The scratch-trained model and about ten variants scored ASR-BLEU 0. The Whisper-based speech-to-text model reached 19.49 BLEU on synthetic test speech and 3.56 on natural speech. The final speech-to-speech model reached 3.25 ASR-BLEU on about 1500 synthetic test utterances per language, 5.1 times the English-unit baseline, and 0.86 ASR-BLEU on natural speech. These scores show feasibility, not usable quality.