forced alignment

Existing forced alignment tools produce only point estimates of segment boundaries without quantifying uncertainty. To address this limitation, we propose the first confidence interval estimation method for forced alignment based on model ensembling and order statistics: ten independently trained segment classification neural networks are aggregated; boundary predictions are centered at the median, and 97.85% confidence intervals are constructed via order statistics. The method supports Praat TextGrid point-tier output and provides an interpretable boundary diagnostic table. This work is the first to integrate neural network ensembling with order statistics for uncertainty modeling in forced alignment. Evaluated on the Buckeye and TIMIT corpora, our approach achieves marginally higher boundary accuracy than single-model baselines while enabling uncertainty-aware linguistic analysis. It has been deployed in real-world speech processing pipelines, facilitating robust, uncertainty-informed phonetic and phonological modeling.

This work addresses the limited word-level time alignment capability of current automatic speech recognition (ASR) models—such as attention-based encoder-decoder (AED) systems and speech large language models—which often lack precise temporal grounding, while conventional alignment methods are constrained by encoder frame rates and offer only modest accuracy. The authors propose a general, training-free, and model-agnostic gradient-driven alignment approach that computes frame-level saliency maps via gradients of token log-probabilities with respect to the input signal under teacher forcing, followed by dynamic programming to decode word boundaries. Applicable to any differentiable ASR model, this method achieves high-precision alignment at the original input sampling rate. Experiments across 16 models on TIMIT and Buckeye datasets show that, although slightly less accurate than strong native aligners, it outperforms them in scenarios where native alignment capabilities are weak, such as with streaming ASR models.

This work addresses the limited parallelizability of the classical dynamic time warping (DTW) algorithm, which suffers from quadratic time and memory complexity. The authors propose Segmental DTW, a novel approach that decomposes global sequence alignment into local subsequence DTW computations that can be executed in parallel, followed by a segment-level dynamic programming step to integrate the partial alignments. This method achieves near-full parallelism while preserving alignment accuracy comparable to standard DTW. Theoretical analysis and empirical evaluation on Chopin Mazurka audio alignment tasks demonstrate that one variant of the proposed method outperforms existing approaches in both computational efficiency and alignment performance.

该研究使用自监督语音表示法提出了两个基于语料库级别的度量方法PCMI和WACS，以解决强制对齐评估中需要手动注释时间戳的问题。

This study addresses the lack of cross-study comparability in forced alignment evaluation caused by inconsistent data partitioning, text normalization, and scoring criteria by constructing a unified, open-source evaluation framework. Methodologically, it introduces a tolerance-based F1 metric to mitigate artificially inflated MAE scores and conducts dual-track evaluations of 21 models under both clean and noisy conditions. Furthermore, it analyzes errors stratified by positional and adjacent-word states while performing fine-grained boundary detection. The findings reveal systematic temporal biases, such as Whisper’s consistent 150ms anticipation. By providing reproducible code and a dynamically updated benchmark, this work establishes a standardized paradigm for forced alignment research.

This study addresses the limited accuracy of existing forced alignment methods under long audio, complex acoustic conditions, and ASR transcription errors by introducing AlignBench, a dedicated evaluation benchmark, and FuseAlign, a Transformer-based model. FuseAlign performs joint audio-visual contextual modeling with millisecond-level boundary refinement. It incorporates an online label correction mechanism via exponential moving average (EMA) snapshots to detect missing words without relying on lexicons or Viterbi decoding. Furthermore, convolutional upsampling and large-scale pseudo-labeled training are employed to enhance robustness. Experimental results demonstrate that FuseAlign significantly outperforms baselines on AlignBench and maintains robust performance in real-world ASR transcription scenarios, validating the critical contribution of each proposed module.

This study investigates the trade-off between efficiency and accuracy in semi-automatic transcription for spoken language corpus construction. Through a two-stage experiment, it compares the performance of expert and novice transcribers on three types of Italian conversational data under both manual and ASR-assisted conditions. The work proposes an integrated analytical framework combining word-level alignment, quality evaluation metrics, and statistical modeling to systematically quantify behavioral differences across transcription workflows. Results demonstrate that ASR substantially increases transcription speed, yet its impact on accuracy varies depending on dialogue type, transcriber expertise, and workflow configuration. The findings provide empirical support for the development of the KIParla corpus, showing that a fine-tuned and optimized semi-automatic pipeline can effectively accelerate annotation while maintaining high transcription quality.

本文针对长音频序列对齐问题，通过应用Hirschberg算法和将语音文本对齐建模为受限随机游走的方法，优化了内存使用并提升了处理速度。

为解决实时语音文本联合建模中的词汇不匹配和延迟问题，StreamAlign通过结合字符级对齐与词级ASR指导实现了流式文本对齐语音分词。

This work addresses the challenge of imprecise control in existing speech editing methods under natural language instructions, which often suffer from semantic ambiguity in specifying edit types, parameters, and target regions. To overcome this, the authors propose a structured editing interface grounded in transcribed text, employing XML-style tags to explicitly denote operation types and anchor them to specific transcript spans or boundaries, thereby constructing a semantic timeline that circumvents the need for explicit time alignment. Building upon this framework, they enhance the continuous autoregressive model dots.tts to support four composable editing dimensions—lexical content, emotion, prosody, and pauses—while preserving contextual integrity in unedited segments. The contributions include the first structured instruction framework for speech editing, a task-oriented data curation pipeline, and doteBench, the first bilingual benchmark for precise evaluation. Experiments demonstrate state-of-the-art instruction-following accuracy and local fidelity across five editing tasks in doteBench, with audio quality comparable to leading open-source systems and no significant degradation in zero-shot TTS error rates or speaker similarity relative to the base model.