Habibi: Laying the Open-Source Foundation of Unified-Dialectal Arabic Speech Synthesis

A notable gap persists in speech synthesis research and development for Arabic dialects, particularly from a unified modeling perspective. Despite its high practical value, the inherent linguistic complexity of Arabic dialects, further compounded by a lack of standardized data, benchmarks, and evaluation guidelines, steers researchers toward safer ground. To bridge this divide, we present Habibi, a suite of specialized and unified text-to-speech models that harnesses existing open-source ASR corpora to support a wide range of high- to low-resource Arabic dialects through linguistically-informed curriculum learning. Our approach outperforms the leading commercial service in generation quality, while maintaining extensibility through effective in-context learning, without requiring text diacritization. We are committed to open-sourcing the model, along with creating the first systematic benchmark for multi-dialect Arabic speech synthesis. Furthermore, by identifying the key challenges in and establishing evaluation standards for the process, we aim to provide a solid groundwork for subsequent research. Resources at https://SWivid.github.io/Habibi/ .

Improved Factorized Neural Transducer Model For text-only Domain Adaptation

End-to-end models, such as the neural Transducer, have been successful in integrating acoustic and linguistic information jointly to achieve excellent recognition performance. However, adapting these models with text-only data is challenging. Factorized neural Transducer (FNT) aims to address this issue by introducing a separate vocabulary decoder to predict the vocabulary, which can effectively perform traditional text data adaptation. Nonetheless, this approach has limitations in fusing acoustic and language information seamlessly. Moreover, a degradation in word error rate (WER) on the general test sets was also observed, leading to doubts about its overall performance. In response to this challenge, we present an improved factorized neural Transducer (IFNT) model structure designed to comprehensively integrate acoustic and language information while enabling effective text adaptation. We evaluate the performance of our proposed methods through in-domain experiments on GigaSpeech and out-of-domain experiments adapting to EuroParl, TED-LIUM, and Medical datasets. After text-only adaptation, IFNT yields 7.9% to 28.5% relative WER improvements over the standard neural Transducer with shallow fusion, and relative WER reductions ranging from 1.6% to 8.2% on the three test sets compared to the FNT model.