Duration-aware pause insertion using pre-trained language model for multi-speaker text-to-speech
The paper presents a strong multi-speaker TTS phrasing approach leveraging speaker-conditioned BERT embeddings and pause duration categories to improve pause insertion precision and synthetic speech rhythm; however, it is out-of-scope for SSI as it focuses on audible speech synthesis only.
Reading guidance
- Verdict
- full-text draft · priority medium · confidence high
- Why it matters
- Demonstrates that explicitly incorporating speaker embeddings and duration categories for respiratory and punctuation-induced pauses significantly improves multi-speaker pause insertion precision and synthetic speech rhythm in TTS systems.
- What to trust
- Basis: full text. Coverage: high. 8 evidence records back the review.
- What is weak
- Restricted to English audiobook-style data with aligned text and audio; requires speaker embeddings; pause duration categorization thresholds could be further improved; silent speech and multi-lingual scenarios unaddressed. Objective evaluation limited to pause position and category prediction precision/recall on LibriTTS; subjective A/B rhythm preference tests conducted only on 16 selected speakers and 277 test sentences. Requires aligned text-audio multi-speaker corpora and speaker embeddings; demonstrated only with FastSpeech 2 TTS backend and English audiobook-style data; no testing on conversational or cross-lingual settings. Focuses on pause insertion and duration categorization for TTS phrasing; does not address silent articulation or silent speech interfaces. Overclaim risk: medium.
- Read before
- SSI review rubric
- Read next
- SSI archive
Axes
- Task
- pause insertion for multi-speaker TTS
- Modality
- text
- Output
- speech-audio
- Vocabulary
- subwords + punctuation
- Metrics
- Respiratory pause precision ~0.569, recall ~0.272, F0.5 ~0.467; Categorized pause insertion respiratory pause precision ~0.575, recall ~0.261, F0.5 ~0.463; categorization of punctuation pauses with precision ~0.848, recall ~0.996, F2 ~0.962; subjective A/B preference tests with 30 listeners and 277 utterances showing consistent rhythm improvement.
- Evaluation mode
- Objective metrics on respiratory and punctuation-induced pause detection precision, recall, and F-scores; subjective A/B preference tests of rhythm conducted with 30 listeners using synthetic speech from FastSpeech 2 with HiFi-GAN vocoder.
- Review confidence
- high
- Overclaim risk
- medium
Expert take
This paper proposes a novel multi-speaker pause insertion framework for TTS that explicitly models speaker-dependent respiratory pause insertion and multi-category pause durations. Leveraging pretrained BERT representations enriched with latent speaker embeddings in BiLSTM decoders, the Respiratory Pause Insertion model alone shows substantial improvement in pause position precision and recall over a conventional baseline. Building on this, the Categorized Pause Insertion model further classifies pauses into brief, medium, and long duration classes for both respiratory and punctuation-induced pauses, enhancing rhythm naturalness as validated through subjective A/B preference tests on synthesized speech using FastSpeech 2. The data underpinning this work is a large-scale English audiobook corpus with over two thousand speakers, aligned text and audio. While objective metric improvements and human preferences support the utility of speaker conditioning and duration-aware phrasing, the scope is limited to English audiobooks with aligned text and does not explore silent articulation or cross-lingual generalization. Deployment relies on obtaining speaker embeddings and aligned corpora, and the approach is demonstrated within a particular TTS architecture. Overall, the paper presents a strong contribution to TTS phrasing emphasizing multi-speaker style adaptation but is peripheral to silent speech interfaces or non-acoustic input paradigms. Future work may explore broader prosodic features and other languages.
True value
Demonstrates that explicitly incorporating speaker embeddings and duration categories for respiratory and punctuation-induced pauses significantly improves multi-speaker pause insertion precision and synthetic speech rhythm in TTS systems.
What changed
Canon before
Pause insertion work largely optimized generic phrasing and ignored speaker-specific pause style in multi-speaker corpora.
Delta from canon
Adds speaker embeddings and explicit duration-based pause categories, enabling joint optimization of phrasing and pause length conditioned on speaker style.
Position in field
A TTS phrasing paper adjacent to SSI only through speech synthesis, not a silent speech interface contribution.
Evidence
“ Although some latent Our approach uses bidirectional encoder representations from trans- grammar and rules are shared among speakers, such differences can formers (BERT) pre-trained on a large-scale text corpus, injecting significantly reduce the accuracy of an RP insertion model when we speaker embeddings to capture various speaker characteristics. ”
author_claim · ABSTRACT · confidence 1.00
“ This paper proposes two multi-speaker pause insertion models: Human speakers usually insert silent pauses into speech to take a the respiratory pause insertion (RPI) model and the categorized breath or show better expression. ”
author_claim · 1. INTRODUCTION · confidence 1.00
“ The main purpose of the RPI model is to quantify the improve- ments brought by the pre-trained BERT and speaker embeddings on LibriTTS includes plenty of long-form sentences containing multi- phrasing. ple silent pauses uttered by more than 2,000 speakers, and thus fits our purpose of evaluating the performance of multi-speaker pause prediction. ”
fact · 2. DATASET · confidence 1.00
“ Two BiLSTM layers are then used to decode the PIPs (category 1) 399,559 9,898 9,979 information from BERT and speaker embeddings, which are initial- PIPs (category 2) 325,327 8,060 7,953 ized randomly and trained with the RPI model. ”
fact · 4. PROPOSED METHOD · confidence 1.00
“ Precision Recall Fβ RPs 0.575 0.261 F0.5 = 0.463 Method A Score Method B p-value PIPs 0.848 0.996 F2 = 0.962 RPI 0.560 vs. ”
metric · 5. EXPERIMENTAL EVALUATIONS · confidence 1.00
“ TTS, especially to show the improvement of inputting categorized pause phonemes, we performed AB preference tests using Fast- Speech 2 as our TTS model with HiFi-GAN [28] as the vocoder. ”
validation_scope · 5. EXPERIMENTAL EVALUATIONS · confidence 1.00
“ In future work, we plan RPs and PIPs, we first selected long-form sentences with a total to explore the effectiveness of incorporating speaker embedding into number of words and punctuation marks between 50 and 60 from the text-processing model of the TTS system for other similar tasks. ”
deployment_claim · 6. CONCLUSION · confidence 1.00
“ Bidirectional en- Pause insertion, also known as phrase break prediction and phras- coder representations from transformers (BERT) [17], one of the ing, is an essential part of TTS systems because proper pauses with well-known pre-trained language models currently, also shows po- natural duration significantly enhance the rhythm and intelligibil- tential for this task. ”
limitation · 6. CONCLUSION · confidence 1.00
Limits
Technical limits
Restricted to English audiobook-style data with aligned text and audio; requires speaker embeddings; pause duration categorization thresholds could be further improved; silent speech and multi-lingual scenarios unaddressed.
Evaluation limits
Objective evaluation limited to pause position and category prediction precision/recall on LibriTTS; subjective A/B rhythm preference tests conducted only on 16 selected speakers and 277 test sentences.
Deployment limits
Requires aligned text-audio multi-speaker corpora and speaker embeddings; demonstrated only with FastSpeech 2 TTS backend and English audiobook-style data; no testing on conversational or cross-lingual settings.
Scope limits
Focuses on pause insertion and duration categorization for TTS phrasing; does not address silent articulation or silent speech interfaces.