Text-to-speech (TTS) systems are an important component in voice-based
e-commerce applications. These applications include end-to-end voice assistant
and customer experience (CX) voice bot. Code-mixed TTS is also relevant in
these applications since the product names are commonly described in English
while the surrounding text is in a regional language. In this work, we describe
our approaches for production quality code-mixed Hindi-English TTS systems
built for e-commerce applications. We propose a data-oriented approach by
utilizing monolingual data sets in individual languages. We leverage a
transliteration model to convert the Roman text into a common Devanagari script
and then combine both datasets for training. We show that such single script
bi-lingual training without any code-mixing works well for pure code-mixed test
sets. We further present an exhaustive evaluation of single-speaker adaptation
and multi-speaker training with Tacotron2 + Waveglow setup to show that the
former approach works better. These approaches are also coupled with transfer
learning and decoder-only fine-tuning to improve performance. We compare these
approaches with the Google TTS and report a positive CMOS score of 0.02 with
the proposed transfer learning approach. We also perform low-resource voice
adaptation experiments to show that a new voice can be onboarded with just 3
hrs of data. This highlights the importance of our pre-trained models in
resource-constrained settings. This subjective evaluation is performed on a
large number of out-of-domain pure code-mixed sentences to demonstrate the high
quality of the systems.

我们提出了适用于电子商务应用的生产级代码混合印地语 - 英语 TTS 系统的方法。我们采用了一种以数据为导向的方法，通过利用各种语言的单语数据集。我们演示了在纯代码混合测试集上，这种单一脚本双语训练的效果很好。我们还通过 Tacotron2 + Waveglow 的设置对单说话人适应和多说话人训练进行了详尽的评估，结果显示前一种方法效果更好。我们还结合了迁移学习和仅解码器微调来提高性能。我们将这些方法与 Google TTS 进行了比较，使用我们提出的迁移学习方法获得了 CMOS 分数为 0.02。我们还进行了低资源语音适应实验，结果表明只需要 3 小时的数据就可以引入新语音。这凸显了我们预先训练模型在资源受限设置中的重要性。我们在大量的领域外纯代码混合句子上进行了主观评估，以展示系统的高质量。

低资源条件下的混合文字语音合成

Code-Mixed Text to Speech Synthesis under Low-Resource Constraints

State-of-the-art keyphrase generation methods generally depend on large
annotated datasets, limiting their performance in domains with limited
annotated data. To overcome this challenge, we design a data-oriented approach
that first identifies salient information using retrieval-based corpus-level
statistics, and then learns a task-specific intermediate representation based
on a pre-trained language model using large-scale unlabeled documents. We
introduce salient span recovery and salient span prediction as denoising
training objectives that condense the intra-article and inter-article knowledge
essential for keyphrase generation. Through experiments on multiple keyphrase
generation benchmarks, we show the effectiveness of the proposed approach for
facilitating low-resource keyphrase generation and zero-shot domain adaptation.
Our method especially benefits the generation of absent keyphrases, approaching
the performance of models trained with large training sets.

本文提出一种数据导向的方法，通过检索式语料统计信息首先确定显著信息，然后使用基于预训练语言模型的任务特定中间表示学习生成关键短语。通过在多个关键短语生成基准测试中对该方法进行实验，证明了该方法对于促进低资源关键短语生成和零 - shot 领域适应性的有效性，尤其有益于缺失的关键短语的学习。