Learning When to Speak: Latency and Quality Trade-offs for Simultaneous Speech-to-Speech Translation with Offline Models (2306.01201v1)

Abstract: Recent work in speech-to-speech translation (S2ST) has focused primarily on offline settings, where the full input utterance is available before any output is given. This, however, is not reasonable in many real-world scenarios. In latency-sensitive applications, rather than waiting for the full utterance, translations should be spoken as soon as the information in the input is present. In this work, we introduce a system for simultaneous S2ST targeting real-world use cases. Our system supports translation from 57 languages to English with tunable parameters for dynamically adjusting the latency of the output -- including four policies for determining when to speak an output sequence. We show that these policies achieve offline-level accuracy with minimal increases in latency over a Greedy (wait-$k$) baseline. We open-source our evaluation code and interactive test script to aid future SimulS2ST research and application development.

• The paper demonstrates that integrating adaptable latency policies with offline SimulST and TTS models can boost BLEU scores by up to 17 points with minimal lag.
• The system architecture uses OpenAI’s Whisper and ElevenLabs API in a cascaded pipeline to balance translation speed and quality.
• Evaluation across languages reveals that tailoring output policies to linguistic structures significantly enhances real-time translation performance.

Analysis of "Learning When to Speak: Latency and Quality Trade-offs for Simultaneous Speech-to-Speech Translation with Offline Models"

This paper addresses the persistent challenge of latency in Simultaneous Speech-to-Speech Translation (SimulS2ST), a scenario where translations need to be initiated before an entire input utterance is delivered. While significant advancements have been made in offline S2ST models, their adaptation to simultaneous applications remains under-explored. Here, the authors present a system designed for real-world SimulS2ST applications that translates from 57 languages into English. The paper focuses on optimizing the trade-off between translation latency and quality, employing adjustable parameters and predefined policies.

System Architecture and Methodology

The framework presented consists of two main components: SimulST (source speech to target text) and TTS (target text to target speech), arranged in a cascaded fashion. The authors utilize OpenAI’s Whisper for the SimulST module and deploy the ElevenLabs API for the TTS task. Unlike traditional approaches, which demand specialized models, this method cleverly leverages offline ST models in an online querying context, which substantially reduces latency without significant quality loss.

To manage latency-accuracy trade-offs, four policies were explored in determining when the system should output speech:

1. Greedy Policy (wait-$k$): Operates under a basic translation scheme, analogous to simplified SimulST paradigms.
2. Offline Policy: Speaks translations only after the entire input has been processed, representing the highest-latency situation.
3. Confidence-Aware Policy (CAP): Utilizes model confidence levels to decide output timing.
4. Consensus Policy (CP): Relies on the similarity of current and previous outputs to inform decisions, aiming to improve stability.

Evaluation and Results

In testing, four languages were considered: Japanese, Spanish, Russian, and Arabic. Results indicated that languages with structural characteristics closer to English, notably Spanish and Russian, demonstrated improved performance in terms of accuracy and reduced latency.

Numerical results are provided as BLEU scores with associated Average Lagging in seconds. Notably, the paper shows that certain policies, such as CAP and CP, can significantly enhance BLEU scores relative to the Greedy approach, with marginal latency increments. Spanish to English translations using CAP ($\gamma = 0.5$) achieved a BLEU increase of 17 points over Greedy, with just one additional second of lag.

The variability between policies across language pairs suggests the necessity for optimizations tailored to specific linguistic contexts, where system parameters are fine-tuned to meet desired latency and accuracy requirements.

Implications and Future Directions

The paper effectively highlights the potential of adopting existing offline models in simultaneous frameworks without extensive modifications. Open-sourcing their evaluation scripts provides an entry point for further research and system optimization in SimulS2ST applications.

Moving forward, this opens numerous pathways for future research:

• Further exploration of policy variants could yield even finer control over latency and quality attributes.
• The intersection of multilingual NLP with simultaneous translation holds promise for enhancing global communication technologies.
• Gathering empirical data from real-time applications could present opportunities to refine model behaviors based on actual usage scenarios.

Conclusion

By demonstrating an efficient methodology for deploying SimulS2ST systems with commendable latency-quality metrics, the paper advances the field of real-time language processing. The pragmatic approach of repurposing existing high-quality offline models for simultaneous use highlights a strategic pivot, beneficial both practically and theoretically, in the ongoing development of multilingual AI communication tools.