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Automatic Speech Recognition (ASR) is playing a vital role in a wide range of real-world applications. However, Commercial ASR solutions are typically “one-size-fits-all” products and clients are inevitably faced with the risk of severe performance degradation in field test. Meanwhile, with new data regulations such as the European Union’s General Data Protection Regulation (GDPR) coming into force, ASR vendors, which traditionally utilize the speech training data in a centralized approach, are becoming increasingly helpless to solve this problem, since accessing clients’ speech data is prohibited. Here, we show that by seamlessly integrating three machine learning paradigms (i.e., T ransfer learning, F ederated learning, and E volutionary learning (TFE)), we can successfully build a win-win ecosystem for ASR clients and vendors and solve all the aforementioned problems plaguing them. Through large-scale quantitative experiments, we show that with TFE, the clients can enjoy far better ASR solutions than the “one-size-fits-all” counterpart, and the vendors can exploit the abundance of clients’ data to effectively refine their own ASR products.

In the last decade, a variety of topic models have been proposed for text engineering. However, except Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA), most of existing topic models are seldom applied or considered in industrial scenarios. This phenomenon is caused by the fact that there are very few convenient tools to support these topic models so far. Intimidated by the demanding expertise and labor of designing and implementing parameter inference algorithms, software engineers are prone to simply resort to PLSA/LDA, without considering whether it is proper for their problem at hand or not.In this paper, we propose a configurable topic modeling framework named Familia, in order to bridge the huge gap between academic research fruits and current industrial practice. Familia supports an important line of topic models that are widely applicable in text engineering scenarios. In order to relieve burdens of software engineers without knowledge of Bayesian networks, Familia is able to conduct automatic parameter inference for a variety of topic models. Simply through changing the data organization of Familia, software engineers are able to easily explore a broad spectrum of existing topic models or even design their own topic models, and find the one that best suits the problem at hand. With its superior extendability, Familia has a novel sampling mechanism that strikes balance between effectiveness and efficiency of parameter inference. Furthermore, Familia is essentially a big topic modeling framework that supports parallel parameter inference and distributed parameter storage. After being open-sourced at Github, Familia rapidly becomes the second most popular project in topic model area, and is widely used in both industrial community and academia. The utilities and necessity of Familia are demonstrated in real-life industrial applications. Familia would significantly enlarge software engineers' arsenal of topic models and pave the way for utilizing highly customized topic models in real-life problems.

Probabilistic topic modeling has been applied in a variety of industrial applications. Training a high-quality model usually requires a massive amount of data to provide comprehensive co-occurrence information for the model to learn. However, industrial data such as medical or financial records are often proprietary or sensitive, which precludes uploading to data centers. Hence, training topic models in industrial scenarios using conventional approaches faces a dilemma: A party (i.e., a company or institute) has to either tolerate data scarcity or sacrifice data privacy. In this article, we propose a framework named Industrial Federated Topic Modeling (iFTM), in which multiple parties collaboratively train a high-quality topic model by simultaneously alleviating data scarcity and maintaining immunity to privacy adversaries. iFTM is inspired by federated learning, supports two representative topic models (i.e., Latent Dirichlet Allocation and SentenceLDA) in industrial applications, and consists of novel techniques such as private Metropolis-Hastings, topic-wise normalization, and heterogeneous model integration. We conduct quantitative evaluations to verify the effectiveness of iFTM and deploy iFTM in two real-life applications to demonstrate its utility. Experimental results verify iFTM’s superiority over conventional topic modeling.

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