SearcharxivSearch

arXiv · 2401.11771

Empowering Communication: Speech Technology for Indian and Western Accents through AI-powered Speech Synthesis

Abstract

Neural Text-to-speech (TTS) synthesis is a powerful technology that can generate speech using neural networks. One of the most remarkable features of TTS synthesis is its capability to produce speech in the voice of different speakers. This paper introduces voice cloning and speech synthesis https://pypi.org/project/voice-cloning/ an open-source python package for helping speech disorders to communicate more effectively as well as for professionals seeking to integrate voice cloning or speech synthesis capabilities into their projects. This package aims to generate synthetic speech that sounds like the natural voice of an individual, but it does not replace the natural human voice. The architecture of the system comprises a speaker verification system, a synthesizer, a vocoder, and noise reduction. Speaker verification system trained on a varied set of speakers to achieve optimal generalization performance without relying on transcriptions. Synthesizer is trained using both audio and transcriptions that generate Mel spectrogram from a text and vocoder which converts the generated Mel Spectrogram into corresponding audio signal. Then the audio signal is processed by a noise reduction algorithm to eliminate unwanted noise and enhance speech clarity. The performance of synthesized speech from seen and unseen speakers are then evaluated using subjective and objective evaluation such as Mean Opinion Score (MOS), Gross Pitch Error (GPE), and Spectral distortion (SD). The model can create speech in distinct voices by including speaker characteristics that are chosen randomly.

Explore related subjects

Keep this discovery

BibTeXRIS

Vinotha R, Hepsiba D, L. D. Vijay Anand, Deepak John Reji. 2024-01-22. Empowering Communication: Speech Technology for Indian and Western Accents through AI-powered Speech Synthesis. https://arxiv.org/abs/2401.11771

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Diarization Error Decomposition Under Pause Annotation Ambiguity

Speaker diarization evaluation is sensitive to ambiguity in pause annotation, which can inflate diarization error rate (DER) or obscure genuine model errors. We show that morphological closing, which has been used for pause-tolerant diarization evaluation, discards segment-level distinctions. Instead, we propose an exact, overlap-aware decomposition of standard DER into a pause-attributable component, consisting of errors compatible with pause filling, and a residual core component that can serve as a proxy for intrinsic diarization errors. The decomposition leaves DER unchanged, while the pause-attributable and core components vary monotonically with the pause threshold and eventually saturate. Experiments spanning synthetic transformations, annotation mismatch, cross-domain evaluation, and tight-boundary diarization show that the decomposition reveals error sources not apparent from standard DER.

eess.AS

Less can be More: What Aspects of Speech Drive End-of-Turn Detection

In conversational AI, detecting when a speaker has finished talking is crucial for natural turn taking. While recent work incorporates semantics, the relative contribution of different modalities remains unclear. We present a controlled ablation of acoustic, prosodic, and semantic signals for streaming end of turn detection using a lightweight trimodal classifier. Under identical training conditions, the acoustic prosodic combination achieves the best balance of accuracy and latency, achieving utterance F1 of 0.93 with 7.8% false alarms at 400ms median latency. Adding text increases premature detections without improving performance. Feature space analysis confirms that prosodic features have the strongest class separability, while text representations overlap substantially. These findings suggest that turn-taking is primarily conveyed through intonation and silence patterns rather than semantic completeness, enabling faster and more reliable systems without expensive text inference.

eess.AS

Downstream-Task-Aware Unified Source Separation

Task-aware unified source separation (TUSS) enables a single model to handle diverse separation tasks by conditioning on input prompts. However, conventional TUSS does not account for downstream task requirements, such as whether the enhanced speech will be used for human listening or automatic speech recognition (ASR). In this paper, we propose a prompt extension framework for TUSS that incorporates downstream task information into the input prompts and switches the loss function according to the given prompt during training, enabling outputs with different signal characteristics at inference time. Specifically, we introduce an ASR-dedicated prompt paired with a regularized loss function that reduces speech artifacts to improve ASR robustness, while the standard prompt is paired with the conventional SNR loss function. Experiments on the LibriSpeech and JNAS corpora demonstrate that the proposed joint-training scheme enables a single model to improve ASR performance over noisy input across a wide range of SNR conditions by selecting the ASR-dedicated prompt, while maintaining general speech enhancement quality when the standard prompt is used.

eess.AS