MSLM-S2ST: Multitask Speech Language Model for Textless Speech-to-Speech Translation

Kernkonzepte

Proposing a Multitask Speech Language Model (MSLM) for textless speech-to-speech translation with speaker style preservation.

Zusammenfassung

The MSLM is a decoder-only speech language model trained in a multitask setting.
It supports multilingual S2ST without relying on text training data.
The model leverages semantic units from HuBERT and acoustic units from EnCodec.
MSLM performs semantic-to-semantic translation and semantic-to-acoustic generation in a single model.
Contributions include supporting multiple translation directions in a single model and achieving high translation quality and speaker style similarity.

Statistiken

Compared to S2ST systems relying on text data, the approach is speech-only training, applicable to unwritten languages.
The proposed MSLM achieves multilingual S2ST with high translation quality and speaker style similarity between English and Spanish.

Zitate

"Our model can achieve multilingual S2ST and demonstrates high translation quality and speaker style similarity between English and Spanish." - Authors

Wichtige Erkenntnisse aus

MSLM-S2ST

by Yifan Peng,I... um arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12408.pdf

Tiefere Fragen

How does the use of semantic units from HuBERT enhance the performance of the MSLM

The use of semantic units from HuBERT enhances the performance of the MSLM in several ways. Firstly, HuBERT provides a robust representation of speech semantics, allowing the MSLM to capture contextual information and long-range dependencies effectively. By leveraging semantic units extracted from HuBERT, the MSLM can better understand and translate the meaning behind spoken language, leading to more accurate translations.
Additionally, using semantic units from HuBERT enables the MSLM to support multilingual speech-to-speech translation without relying on text data. This is crucial for languages that do not have a writing system or textual resources available for training models. The semantic representations provided by HuBERT allow for language-agnostic processing and facilitate cross-lingual communication.
Moreover, incorporating semantic units from HuBERT into the MSLM helps improve speaker style preservation during translation. By understanding the underlying semantics of speech utterances, the model can maintain consistency in speaker style across different languages, enhancing overall translation quality and user experience.

What are the potential risks associated with using the MSLM for cross-lingual communication

While MSLM offers significant benefits for cross-lingual communication through its multitask learning framework and style-preserved S2ST capabilities, there are potential risks associated with its usage:

Miscommunication: One risk is related to potential misinterpretation or mistranslation of spoken content by the model. Inaccurate translations could lead to misunderstandings between speakers communicating in different languages.

Privacy Concerns: Since speech contains personal information such as voice characteristics and linguistic patterns unique to individuals, there may be privacy concerns when using an automated system like MSLM for sensitive conversations.

Bias Amplification: There is a risk that biases present in training data could be amplified or perpetuated by the model during cross-lingual communication. Biased translations could reinforce stereotypes or discriminatory language practices.

Security Vulnerabilities: As with any AI system handling sensitive information, there is a risk of security vulnerabilities that could potentially expose confidential conversations or compromise data integrity during translation processes.

How can the concept of bidirectional translation benefit other areas beyond speech-to-speech translation

The concept of bidirectional translation inherent in models like MSLM can benefit various areas beyond just speech-to-speech translation:

Machine Translation: Bidirectional translation techniques can enhance traditional machine translation systems by improving accuracy and fluency in translating text between multiple languages both ways (e.g., English-Spanish and Spanish-English).

Cross-Lingual Information Retrieval: Bidirectional approaches enable more effective retrieval of information across different languages on search engines or databases where users might input queries in one language but expect results in another.

3Dialogue Systems: In conversational AI applications like chatbots or virtual assistants operating across multiple languages bidirectional translation allows seamless interaction between users speaking different languages ensuring smooth dialogue flow.
4Multimodal Communication: Incorporating bidirectional translation methods into multimodal systems involving text,speech,and images facilitates comprehensive communication experiences enabling users to interact seamlessly regardless 0f their preferred mode 0f expression.
5Cultural Exchange Platforms: Platforms promoting cultural exchange,business collaboration,and international cooperation can leverage bidirectional translati0n t0 bridge linguistic gaps,facilitating meaningful interactions among participants speaking diverse langUages.