The goal of this work is to recover articulatory information from the speech signal by acoustic-to-articulatory inversion. One of the main difficulties with inversion is that the problem is underdetermined and inversion methods generally offer no guarantee on the phonetical realism of the inverse solutions. A way to adress this issue is to use additional phonetic constraints. Knowledge of the phonetic caracteristics of French vowels enable the derivation of reasonable articulatory domains in the space of Maeda parameters: given the formants frequencies (F1,F2,F3) of a speech sample, and thus the vowel identity, an "ideal" articulatory domain can be derived. The space of formants frequencies is partitioned into vowels, using either speaker-specific data or generic information on formants. Then, to each articulatory vector can be associated a phonetic score varying with the distance to the "ideal domain" associated with the corresponding vowel. Inversion experiments were conducted on isolated vowels and vowel-to-vowel transitions. Articulatory parameters were compared with those obtained without using these constraints and those measured from X-ray data.
Improving generalization of vocal tract feature reconstruction: from augmented acoustic inversion to articulatory feature reconstruction without articulatory data
Attention model for articulatory features detection
Sciforce
发音特征以及语音标音在语音相关任务中起着重要作用：计算机辅助发音训练，文本到语音转换（TTS），研究语音产生机制，低资源语言的语音识别。近年来，语音相关任务的端到端方法得到了很大的吸引力。我们将Listen，Attend and Spell~（LAS）~ \cite{Chan-LAS2016}结构应用于小型训练集上的音素识别，如TIMIT~ \cite{TIMIT-1992}。此外，我们引入了一种新的解码技术，允许训练的方式和地方的发音检测器端到端使用注意力模型。我们还探讨了多任务学习环境中的联合音素识别和发音特征检测。
ARTI-6: Towards Six-dimensional Articulatory Speech Encoding
Signal Analysis and Interpretation Lab, University of Southern California（南加州大学信号分析与解释实验室）；Department of Linguistics, University of Southern California（南加州大学语言学系）
An Audio-textual Diffusion Model For Converting Speech Signals Into Ultrasound Tongue Imaging Data
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems（粤港澳人机协同智能系统联合实验室）
Multilingual and crosslingual speech recognition using phonological-vector based phone embeddings
Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）
语音特征（PF）的使用潜在地允许特定语言的音素在训练中保持链接，这对于用于低资源语言的多语言和跨语言语音识别方法的信息共享是非常期望的。以前的方法在使用语音特征时的缺点是，以自底向上的方式提取声学到PF本身是困难的。在本文中，我们建议将音位驱动的音素嵌入（自上而下）和基于深度神经网络（DNN）的声学特征提取（自下而上）结合起来，以计算音素概率。这种新方法被称为JoinAP（Joining of Acoustics and Phonology）。值得注意的是，语音识别不需要从声学特征到音位特征的反演。对于IPA（International Phonetic Alphabet）表中的每个音素，我们将其音素特征编码为一个音素向量，然后对该音素向量进行线性或非线性变换，得到音素嵌入。在CommonVoice数据集（德语、法语、西班牙语和意大利语）和AISHLL-1数据集（普通话）上进行了一系列多语种和跨语种（包括zero-shot和Few-Shot）语音识别实验，证明了非线性音素嵌入的JoinAP方法优于线性音素嵌入的JoinAP方法和传统的平面音素嵌入方法。
