LASE: Language-Adversarial Speaker Encoding for Indic Cross-Script Identity Preservation
Abstract
A speaker encoder used in multilingual voice cloning should treat the same speaker identically regardless of which script the audio was uttered in. Off-the-shelf encoders do not, and the failure is accent-conditional. On a 1043-pair Western-accented voice corpus across English, Hindi, Telugu, and Tamil, WavLM-base-plus-sv loses 0.082 absolute cosine similarity when the same voice changes script and ECAPA-TDNN loses 0.105. On a 1369-pair Indian-accented voice corpus, the gap shrinks to 0.006 (WavLM-SV) and 0.044 (ECAPA-TDNN). The leak is largest where it matters most for cross-script TTS: when a system projects a non-Indic-trained voice into Indic scripts. We present LASE (Language-Adversarial Speaker Encoder), a small projection head over frozen WavLM-base-plus trained with two losses: a supervised contrastive loss over voice identity, and a gradient-reversal cross-entropy against a 4-language classifier that pushes the embedding to be language-uninformative while remaining speaker-informative. Trained on 1118 quality-gated cross-script pairs synthesised from 8 commercial multilingual voices, LASE's residual gap is consistent with zero on both corpora (Delta = 0.013 Western, Delta = 0.026 Indian; both bootstrap 95% CIs include zero) and amplifies the cross-script-vs-floor margin 2.4-2.7x over both baselines. An ECAPA+GRL ablation shows the GRL objective improves either backbone but the WavLM choice contributes too. In synthetic multi-speaker diarisation, LASE matches ECAPA-TDNN on cross-script speaker recall (0.788 vs 0.789) with ~100x less training data. We release the r1 checkpoint, both corpora, and the bootstrap recipe.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents LASE, a novel approach to cross-script identity preservation in multilingual voice cloning, demonstrating significant advancements in disentangling language from speaker identity and providing valuable resources for future research. The methodology and results contribute meaningfully to the field of audio processing and speaker recognition, particularly in the context of Indic languages.
Methodology
The paper introduces a novel approach using a Language-Adversarial Speaker Encoder (LASE) that effectively disentangles language from speaker identity in multilingual voice cloning tasks. The methodology employs a gradient-reversal layer and a supervised contrastive loss to create a speaker embedding that is invariant to language, which is a significant advancement in the field. The architecture is well-defined, consisting of a frozen WavLM-base-plus backbone and a trainable projection head, which allows for efficient training and effective performance on cross-script tasks.
Results & evaluation
The experiments are robust, utilizing two distinct corpora to evaluate the performance of LASE against established baselines (WavLM-base-plus-sv and ECAPA-TDNN). The results demonstrate a significant reduction in the identity gap across scripts, with LASE achieving a gap of 0.013 compared to 0.082 and 0.105 for the baselines. The paper also includes a thorough analysis of the training dynamics and presents a synthetic multi-speaker diarisation benchmark, showing that LASE can match ECAPA-TDNN's performance with significantly less training data.
Reproducibility
The authors provide a comprehensive set of resources, including the model weights, training corpus, and evaluation scripts, which enhances reproducibility. The detailed description of the training process, loss functions, and hyperparameters further supports the ability of other researchers to replicate the results.
Limitations
The study relies solely on synthetic data generated by ElevenLabs, which may not fully capture the complexities of natural human speech. Additionally, the held-out set shares voices with the training data, limiting the generalization assessment. The paper also acknowledges that the model's performance on real-world data and new voices remains to be evaluated.
Broader impact
The implications of this work are significant for applications in multilingual voice cloning, speaker verification, and diarisation systems, particularly in contexts involving Indian languages. The ability to maintain speaker identity across different scripts can enhance user experience in customer support, content creation, and accessibility technologies. The paper presents LASE, a novel approach to cross-script identity preservation in multilingual voice cloning, demonstrating significant advancements in disentangling language from speaker identity and providing valuable resources for future research. The methodology and results contribute meaningfully to the field of audio processing and speaker recognition, particularly in the context of Indic languages.
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