TTSDS2: Resources and Benchmark for Evaluating Human-Quality Text to Speech Systems
- URL: http://arxiv.org/abs/2506.19441v1
- Date: Tue, 24 Jun 2025 09:12:02 GMT
- Title: TTSDS2: Resources and Benchmark for Evaluating Human-Quality Text to Speech Systems
- Abstract summary: We introduce Text to Speech Distribution Score 2 (TTSDS2), a more robust and improved version of TTSDS.<n>TTSDS2 is the only one out of 16 compared metrics to correlate with a Spearman correlation above 0.50 for every domain and subjective score evaluated.<n>We also release a range of resources for evaluating synthetic speech close to real speech: A dataset with over 11,000 subjective opinion score ratings; a pipeline for continually recreating a multilingual test dataset to avoid data leakage.
- Score: 13.307889110301502
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Evaluation of Text to Speech (TTS) systems is challenging and resource-intensive. Subjective metrics such as Mean Opinion Score (MOS) are not easily comparable between works. Objective metrics are frequently used, but rarely validated against subjective ones. Both kinds of metrics are challenged by recent TTS systems capable of producing synthetic speech indistinguishable from real speech. In this work, we introduce Text to Speech Distribution Score 2 (TTSDS2), a more robust and improved version of TTSDS. Across a range of domains and languages, it is the only one out of 16 compared metrics to correlate with a Spearman correlation above 0.50 for every domain and subjective score evaluated. We also release a range of resources for evaluating synthetic speech close to real speech: A dataset with over 11,000 subjective opinion score ratings; a pipeline for continually recreating a multilingual test dataset to avoid data leakage; and a continually updated benchmark for TTS in 14 languages.
Related papers
- Domain-Specific Evaluation of Text-to-Speech Systems: A Multi-Metric Benchmarking Study [1.224954637705144]
This paper presents a reproducible, multi-metric benchmarking framework for systematic evaluation of modern TTS systems.<n>Four state-of-the-art TTS systems -- Indic-Parler-TTS, MMS-TTS, Microsoft Edge TTS, and Google Gemini TTS -- are evaluated.<n>Results reveal substantial variation in TTS performance across speech domains, with emotional speech consistently presenting the greatest synthesis challenge.
arXiv Detail & Related papers (2026-08-03T13:49:11Z) - Benchmarking Speech-to-Speech Translation Models [55.00303727199927]
Speech-to-speech translation (S2ST) has advanced rapidly, but offline evaluation lacks a unified protocol.<n>We introduce a benchmarking framework integrating 46 metrics across eight dimensions.<n>We deploy it on 1,248 model-language configurations from FLEURS and CVSS.
arXiv Detail & Related papers (2026-06-02T07:01:33Z) - MUSCAT: MUltilingual, SCientific ConversATion Benchmark [60.87925076316812]
The goal of multilingual speech technology is to facilitate seamless communication between individuals speaking different languages.<n>To create this experience, speech technology needs to address several challenges: Handling mixed multilingual input, specific vocabulary, and code-switching.<n>We propose a new benchmark to evaluate current Automatic Speech Recognition (ASR) systems, whether they are able to handle these challenges.
arXiv Detail & Related papers (2026-04-17T10:39:01Z) - Improving Code-Switching Speech Recognition with TTS Data Augmentation [58.34842693152991]
This paper explores multilingual text-to-speech (TTS) models as an effective data augmentation technique to address this shortage.<n>We fine-tune the multilingual CosyVoice2 TTS model on the SEAME dataset to generate synthetic conversational Chinese-English code-switching speech.
arXiv Detail & Related papers (2026-01-02T10:11:51Z) - RosettaSpeech: Zero-Shot Speech-to-Speech Translation from Monolingual Data [30.27234062544891]
This paper introduces RosettaSpeech, a novel and simplified framework for zero-shot speech-to-speech translation (S2ST)<n>While our method leverages the linguistic knowledge inherent in text-based NMT models, it strictly eliminates the need for parallel speech-to-speech pairs.<n>Our model uses text as an intermediate bridge during training but functions as a direct, end-to-end speech-to-speech model at inference.
arXiv Detail & Related papers (2025-11-26T02:02:20Z) - LibriQuote: A Speech Dataset of Fictional Character Utterances for Expressive Zero-Shot Speech Synthesis [9.933931058173014]
We introduce the LibriQuote dataset, an English corpus derived from read audiobooks.<n>The training dataset includes 12.7K hours of read, non-expressive speech and 5.3K hours of mostly expressive speech drawn from character quotations.<n>We also provide a challenging 7.5 hour test set intended for benchmarking TTS systems.
arXiv Detail & Related papers (2025-09-04T10:05:06Z) - AHELM: A Holistic Evaluation of Audio-Language Models [78.20477815156484]
multimodal audio-language models (ALMs) take interleaved audio and text as input and output text.<n>AHELM is a benchmark that aggregates various datasets -- including 2 new synthetic audio-text datasets called PARADE and CoRe-Bench.<n>We also standardize the prompts, inference parameters, and evaluation metrics to ensure equitable comparisons across models.
arXiv Detail & Related papers (2025-08-29T07:40:39Z) - Synth4Kws: Synthesized Speech for User Defined Keyword Spotting in Low Resource Environments [8.103855990028842]
We introduce Synth4Kws - a framework to leverage Text to Speech (TTS) synthesized data for custom KWS.
We found increasing TTS phrase diversity and utterance sampling monotonically improves model performance.
Our experiments are based on English and single word utterances but the findings generalize to i18n languages.
arXiv Detail & Related papers (2024-07-23T21:05:44Z) - TTSDS -- Text-to-Speech Distribution Score [9.380879437204277]
Many recently published Text-to-Speech (TTS) systems produce audio close to real speech.<n>We propose evaluating the quality of synthetic speech as a combination of multiple factors such as prosody, speaker identity, and intelligibility.<n>We benchmark 35 TTS systems developed between 2008 and 2024 and show that our score computed as an unweighted average of factors strongly correlates with the human evaluations.
arXiv Detail & Related papers (2024-07-17T16:30:27Z) - Code-Switching Text Generation and Injection in Mandarin-English ASR [57.57570417273262]
We investigate text generation and injection for improving the performance of an industry commonly-used streaming model, Transformer-Transducer (T-T)
We first propose a strategy to generate code-switching text data and then investigate injecting generated text into T-T model explicitly by Text-To-Speech (TTS) conversion or implicitly by tying speech and text latent spaces.
Experimental results on the T-T model trained with a dataset containing 1,800 hours of real Mandarin-English code-switched speech show that our approaches to inject generated code-switching text significantly boost the performance of T-T models.
arXiv Detail & Related papers (2023-03-20T09:13:27Z) - A Vector Quantized Approach for Text to Speech Synthesis on Real-World
Spontaneous Speech [94.64927912924087]
We train TTS systems using real-world speech from YouTube and podcasts.
Recent Text-to-Speech architecture is designed for multiple code generation and monotonic alignment.
We show thatRecent Text-to-Speech architecture outperforms existing TTS systems in several objective and subjective measures.
arXiv Detail & Related papers (2023-02-08T17:34:32Z) - Learning to Speak from Text: Zero-Shot Multilingual Text-to-Speech with
Unsupervised Text Pretraining [65.30528567491984]
This paper proposes a method for zero-shot multilingual TTS using text-only data for the target language.
The use of text-only data allows the development of TTS systems for low-resource languages.
Evaluation results demonstrate highly intelligible zero-shot TTS with a character error rate of less than 12% for an unseen language.
arXiv Detail & Related papers (2023-01-30T00:53:50Z) - SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic
Mistakes [93.19166902594168]
We propose SESCORE2, a self-supervised approach for training a model-based metric for text generation evaluation.
Key concept is to synthesize realistic model mistakes by perturbing sentences retrieved from a corpus.
We evaluate SESCORE2 and previous methods on four text generation tasks across three languages.
arXiv Detail & Related papers (2022-12-19T09:02:16Z) - BLASER: A Text-Free Speech-to-Speech Translation Evaluation Metric [66.73705349465207]
End-to-end speech-to-speech translation (S2ST) is generally evaluated with text-based metrics.
We propose a text-free evaluation metric for end-to-end S2ST, named BLASER, to avoid the dependency on ASR systems.
arXiv Detail & Related papers (2022-12-16T14:00:26Z) - Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised
Learning for Text-To-Speech [37.942466944970704]
This paper proposes Virtuoso, a massively multilingual speech-text joint semi-supervised learning framework for text-to-speech synthesis (TTS) models.
To train a TTS model from various types of speech and text data, different training schemes are designed to handle supervised (TTS and ASR data) and unsupervised (untranscribed speech and unspoken text) datasets.
Experimental evaluation shows that multilingual TTS models trained on Virtuoso can achieve significantly better naturalness and intelligibility than baseline ones in seen languages.
arXiv Detail & Related papers (2022-10-27T14:09:48Z) - The Conversational Short-phrase Speaker Diarization (CSSD) Task:
Dataset, Evaluation Metric and Baselines [63.86406909879314]
This paper describes the Conversational Short-phrases Speaker Diarization (CSSD) task.
It consists of training and testing datasets, evaluation metric and baselines.
In the metric aspect, we design the new conversational DER (CDER) evaluation metric, which calculates the SD accuracy at the utterance level.
arXiv Detail & Related papers (2022-08-17T03:26:23Z) - NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level
Quality [123.97136358092585]
We develop a TTS system called NaturalSpeech that achieves human-level quality on a benchmark dataset.
Specifically, we leverage a variational autoencoder (VAE) for end-to-end text to waveform generation.
Experiment evaluations on popular LJSpeech dataset show that our proposed NaturalSpeech achieves -0.01 CMOS to human recordings at the sentence level.
arXiv Detail & Related papers (2022-05-09T16:57:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.