SpeechAlign: a Framework for Speech Translation Alignment Evaluation

Abstract: Speech-to-Speech and Speech-to-Text translation are currently dynamic areas of research. In our commitment to advance these fields, we present SpeechAlign, a framework designed to evaluate the underexplored field of source-target alignment in speech models. The SpeechAlign framework has two core components. First, to tackle the absence of suitable evaluation datasets, we introduce the Speech Gold Alignment dataset, built upon a English-German text translation gold alignment dataset. Secondly, we introduce two novel metrics, Speech Alignment Error Rate (SAER) and Time-weighted Speech Alignment Error Rate (TW-SAER), which enable the evaluation of alignment quality within speech models. While the former gives equal importance to each word, the latter assigns weights based on the length of the words in the speech signal. By publishing SpeechAlign we provide an accessible evaluation framework for model assessment, and we employ it to benchmark open-source Speech Translation models. In doing so, we contribute to the ongoing research progress within the fields of Speech-to-Speech and Speech-to-Text translation.

• The paper introduces SpeechAlign, a framework that benchmarks speech translation models using an extended speech alignment dataset.
• The study proposes two new metrics, SAER and TW-SAER, to quantify alignment quality accounting for word durations.
• Empirical results show a clear link between model size, improved alignment, and translation performance, guiding future enhancements.

An Analysis of "SpeechAlign: a Framework for Speech Translation Alignment"

The paper "SpeechAlign: a Framework for Speech Translation Alignment" presents a framework aimed at addressing a critical gap in the development and evaluation of speech translation models. This work tackles the often-overlooked task of evaluating source-target alignment in speech translation. As the fields of Speech-to-Speech Translation (S2ST) and Speech-to-Text Translation (S2TT) continue to advance, understanding the alignment capabilities of these systems becomes increasingly significant. This framework, SpeechAlign, introduces novel elements that enhance the evaluation process and aims to enable more efficient assessment and improvement of speech models.

Key Contributions

The primary contributions of this paper include the introduction of a benchmarking framework for speech translation models that includes:

1. The Speech Gold Alignment Dataset: This dataset extends an existing text translation gold alignment dataset for English-German by incorporating synthetic speech generated via a Text-to-Speech (TTS) model. This allows the dataset to serve as a critical resource for evaluating alignment tasks in both S2TT and S2ST contexts.
2. New Metrics for Alignment Evaluation: The authors propose two metrics: Speech Alignment Error Rate (SAER) and Time-weighted Speech Alignment Error Rate (TW-SAER). These metrics are designed to evaluate the alignment quality by assessing the match between model-generated and gold-standard alignments, accounting for word durations in the speech signal.
3. An Open-source Framework: SpeechAlign includes a pipeline for processing token-to-token contributions maps and deriving word-to-word alignments, ultimately enabling the computation of SAER and TW-SAER metrics.

Empirical Results

The framework is used to benchmark various sizes of the Whisper model on the task of De-En S2TT. The authors report SAER and TW-SAER metrics alongside BLEU scores, finding a correlation between model size, alignment quality, and translation performance. This relationship indicates that larger models tend to produce better alignments and translation outputs, suggesting a possible need for probing smaller models with advanced techniques to understand their inner workings.

Implications and Future Directions

The introduction of the Speech Gold Alignment dataset and new metrics like SAER and TW-SAER holds significant implications for the field of speech translation. This work provides resources to systematically evaluate and compare speech translation models, potentiating more rapid and effective enhancements in model architectures. Furthermore, by facilitating detailed alignment evaluation, the framework promises to contribute deeper insights into how these models transcribe and translate speech, potentially informing refinements in training methodologies and architecture design.

Looking forward, future research may leverage SpeechAlign to develop more sophisticated interpretability methods and analyze the alignment behavior of multimodal models. Additionally, expanding the dataset to include more languages could further generalize the utility of SpeechAlign across various linguistic contexts.

In summary, the SpeechAlign framework represents an impactful advance in the evaluation methodologies for speech translation, providing critical tools to enhance the understanding and development of state-of-the-art translation systems.