Developing High-Quality Text-to-Speech Synthesizers for 13 Indian Languages Using Signal Processing-Aided Alignments

Keskeiset käsitteet

Integrating signal processing cues with deep learning techniques can produce accurate phone alignments, leading to better duration modeling and higher-quality text-to-speech synthesis for Indian languages.

Tiivistelmä

This paper presents a method for developing high-quality end-to-end (E2E) text-to-speech (TTS) systems for 13 Indian languages by seamlessly integrating signal processing cues with deep learning techniques. The focus is on improving the duration prediction and thereby the synthesis quality of the E2E TTS systems by correcting the phone alignments of the training data.

The authors use the FastSpeech2 architecture as the mel-spectrogram generation model and the HiFi-GAN vocoder for speech reconstruction. They compare the performance of systems trained with different alignment techniques: a teacher model, Montreal Forced Aligner (MFA), and a hybrid HMM-GD-DNN segmentation (HS) approach that combines signal processing cues and deep learning.

Experiments on the Hindi male dataset show that the HS-based system outperforms the other alignment approaches, especially in low-resource scenarios. The authors also evaluate the proposed systems against the existing best TTS systems available for 13 Indian languages and find that the HS-based systems perform better on average, with a 62.63% preference from native listeners.

The key highlights are:

• Accurate phone alignments obtained using signal processing cues in tandem with deep learning lead to better duration modeling and higher-quality synthesis.
• The HS-based FastSpeech2 system outperforms systems trained with teacher model and MFA alignments, especially in low-resource scenarios.
• The proposed systems perform better than the existing state-of-the-art TTS systems for 13 Indian languages on average.

Tilastot

The average absolute boundary difference between manual alignments and those obtained using MFA is 11.88 ms, while for the HS approach it is 4.40 ms.
The MCD score for the HS-based system is 6.58, comparable to 6.56 for the teacher model and 6.61 for the MFA system on the Hindi male dataset with full data.
In the low-resource scenario with 1 hour of data, the MCD scores are 7.21 for HS, 7.30 for MFA, and 6.95 for the VITS model.

Lainaukset

"Integrating signal processing cues with deep learning techniques can produce accurate phone alignments, leading to better duration modeling and higher-quality text-to-speech synthesis for Indian languages."
"The HS-based FastSpeech2 system outperforms systems trained with teacher model and MFA alignments, especially in low-resource scenarios."
"The proposed systems perform better than the existing state-of-the-art TTS systems for 13 Indian languages on average."

Tärkeimmät oivallukset

Towards Developing State-of-the-Art TTS Synthesisers for 13 Indian Languages with Signal Processing aided Alignments

by Anusha Praka... klo arxiv.org 09-19-2024

https://arxiv.org/pdf/2210.17153.pdf

Syvällisempiä Kysymyksiä

How can the proposed approach be extended to model other prosodic features like stress and pitch for improved text-to-speech synthesis?

The proposed approach, which integrates signal processing cues with deep learning techniques for accurate phone alignments, can be extended to model other prosodic features such as stress and pitch by incorporating additional predictors and features into the end-to-end (E2E) text-to-speech (TTS) synthesis framework.

Incorporating Stress Modeling: Stress is a crucial prosodic feature that affects the intelligibility and naturalness of synthesized speech. To model stress, the system can be enhanced by introducing a stress predictor that analyzes the linguistic context of words and phrases. This predictor can utilize linguistic features such as part-of-speech tags, syllable structure, and word frequency to determine which syllables should be stressed. By integrating this information into the FastSpeech2 architecture, the model can adjust the duration and intensity of stressed syllables, leading to more natural-sounding speech.

Enhancing Pitch Prediction: Pitch is another vital aspect of prosody that conveys emotions and intentions in speech. The proposed system can be extended to include a pitch predictor that generates pitch contours based on linguistic and contextual cues. This can be achieved by training a separate neural network to predict pitch values for each phoneme or syllable, which can then be incorporated into the mel-spectrogram generation process. By using signal processing techniques to refine pitch predictions, the system can produce more expressive and varied intonations.

Multi-Task Learning: A multi-task learning framework can be employed where the model simultaneously learns to predict duration, pitch, and stress. This approach allows the model to leverage shared representations and improve overall synthesis quality. By training on a diverse dataset that includes various prosodic features, the system can generalize better across different languages and speakers.

Evaluation and Fine-Tuning: Continuous evaluation and fine-tuning of the model using subjective measures, such as mean opinion scores (MOS), can help in optimizing the prosodic features. By collecting feedback from native speakers, the system can be iteratively improved to enhance the naturalness and expressiveness of the synthesized speech.

What are the potential challenges in scaling the hybrid segmentation approach to a larger number of languages and speakers?

Scaling the hybrid segmentation approach to a larger number of languages and speakers presents several challenges:

Linguistic Diversity: The linguistic diversity of Indian languages, which includes various phonetic and prosodic characteristics, poses a significant challenge. Each language may have unique phonemes, syllable structures, and stress patterns that require tailored segmentation techniques. Developing a universal hybrid segmentation model that effectively captures these nuances across multiple languages can be complex.

Data Availability: The effectiveness of the hybrid segmentation approach relies heavily on the availability of high-quality, annotated speech data. Many Indian languages have limited resources, which can hinder the training of robust models. Collecting sufficient data for each language, especially for low-resource languages, is a significant challenge.

Speaker Variability: Variability in speaker characteristics, such as accent, pitch, and speaking style, can affect the accuracy of alignments. The hybrid segmentation approach must be adaptable to different speakers to ensure consistent performance. This may require additional training data and fine-tuning for each speaker or dialect.

Computational Resources: The hybrid segmentation approach combines signal processing and machine learning techniques, which can be computationally intensive. Scaling the system to handle multiple languages and speakers may require significant computational resources, including powerful GPUs and optimized algorithms to ensure efficient processing.

Integration with Existing Systems: Integrating the hybrid segmentation approach with existing TTS systems can be challenging, especially if those systems are based on different architectures or alignment techniques. Ensuring compatibility and optimizing the workflow for seamless integration will require careful planning and development.

Can the insights from this work be applied to improve the performance of other direct text-to-speech E2E systems beyond FastSpeech2?

Yes, the insights from this work can be applied to improve the performance of other direct text-to-speech (TTS) E2E systems beyond FastSpeech2 in several ways:

Alignment Techniques: The hybrid segmentation approach, which combines signal processing cues with machine learning-based alignments, can be adapted for use in other E2E systems such as Tacotron, Glow-TTS, and VITS. By enhancing the accuracy of phone alignments, these systems can achieve better duration modeling and, consequently, improved synthesis quality.

Prosody Modeling: The methodologies developed for accurately modeling prosodic features, such as duration, stress, and pitch, can be integrated into other E2E architectures. For instance, systems like Tacotron2 can benefit from the proposed stress and pitch predictors, leading to more expressive and natural-sounding speech synthesis.

Low-Resource Adaptation: The findings regarding the effectiveness of the hybrid segmentation approach in low-resource scenarios can inform the development of TTS systems for other languages with limited data. By leveraging signal processing techniques to enhance alignments, other systems can improve their performance even with smaller datasets.

Multi-Lingual and Cross-Lingual Applications: The insights gained from developing TTS systems for 13 Indian languages can be extended to other multilingual and cross-lingual TTS applications. The techniques for accurate alignments and prosody modeling can be adapted to cater to the specific needs of different languages, enhancing the overall quality of synthesized speech.

Generalization of Techniques: The principles of combining signal processing with deep learning can be generalized to other areas of speech synthesis and processing. This approach can lead to innovations in various TTS architectures, improving their robustness and adaptability to diverse linguistic contexts.

By applying these insights, researchers and developers can enhance the capabilities of various E2E TTS systems, leading to more natural, intelligible, and expressive synthesized speech across different languages and applications.