Why Speech Translation Models Need a New Evaluation Playbook

Speech translation technology has achieved notable strides in capturing nuances such as speaker gender and prosody. Yet, evaluation metrics haven't kept pace. The gap is significant, and it signals a broader issue in the assessment of speech-specific phenomena.

Metrics Miss the Mark

Recent studies have highlighted the inadequacies of both text-based and speech-based quality estimation metrics. Even with direct access to speech signals, these metrics fall short. Visualize this: models decode complex speech characteristics, but evaluators remain blind to these intricacies. It's like critiquing a symphony with earplugs on.

Enter SpeechCOMET, a new family of quality estimation models armed with speech encoders. When tested against a state-of-the-art SpeechLLM, SpeechCOMET performed admirably, matching or surpassing its text-based counterparts. However, the consistent assessment of speech-specific features remains elusive.

Why the Struggle?

Three primary reasons underpin this struggle. First, current encoders fail to reliably preserve speech-specific features. Second, models have a tendency to overlook the speech source signal. Lastly, the quality estimation training data is akin to a sparsely stocked pantry, lacking enough relevant examples to create reliable models. Numbers in context: without purposeful training data, progress stalls.

A Call for Change

The solution lies in developing dedicated speech-specific training data and models genuinely conditioned on speech. It's a straightforward proposition, yet the execution is complex. But here's the kicker: if advancements in speech technology are to reflect the rich diversity of human communication, then evaluation metrics must evolve. Otherwise, we risk celebrating incomplete victories.

So why should readers care? Because the future of communication technology hinges on our ability to measure what truly matters. Are we content with half-baked evaluations, or will we demand metrics that honor the full spectrum of speech? The trend is clearer when you see it. It's time for a new playbook.