Federated learning with differential privacy, i.e. private federated learning (PFL), makes it possible to train models on private data distributed across users’ devices without harming privacy. PFL is efficient for models, such as neural networks, that have a fixed number of parameters, and thus a fixed-dimensional gradient vector. Such models include neural-net language models, but not tokenizers, the topic of this work. Training a tokenizer requires frequencies of words from an unlimited vocabulary, and existing methods for finding an unlimited vocabulary need a separate privacy budget.
A…Apple Machine Learning Research
Streaming On-Device Detection of Device Directed Speech from Voice and Touch-Based Invocation
When interacting with smart devices such as mobile- phones or wearables, the user typically invokes a virtual assistant (VA) by saying a keyword or by pressing a but- ton on the device. However, in many cases, the VA can accidentally be invoked by the keyword-like speech or ac- cidental button press, which may have implications on user experience and privacy. To this end, we propose an acous- tic false-trigger-mitigation (FTM) approach for on-device device-directed speech detection that simultaneously handles the voice-trigger and touch-based invocation. To facilitate the model deployment…Apple Machine Learning Research
Bilingual End-to-End ASR with Byte-Level Subwords
In this paper, we investigate how the output representation of an end-to-end neural network affects multilingual automatic speech recognition (ASR). We study different representations including character-level, byte-level, byte pair encoding (BPE), and byte- level byte pair encoding (BBPE) representations, and analyze their strengths and weaknesses. We focus on developing a single end-to- end model to support utterance-based bilingual ASR, where speakers do not alternate between two languages in a single utterance but may change languages across utterances. We conduct our experiments on…Apple Machine Learning Research
Utilizing Imperfect Synthetic Data to Improve Speech Recognition
With recent advances in speech synthesis, synthetic data is becoming a viable alternative to real data for training speech recognition models. However, machine learning with synthetic data is not trivial due to the gap between the synthetic and the real data distributions. Synthetic datasets may contain artifacts that do not exist in real data such as structured noise, content errors, or unrealistic speaking styles. Moreover, the synthesis process may introduce a bias due to uneven sampling of the data manifold. We propose two novel techniques during training to mitigate the problems due to…Apple Machine Learning Research
Data Incubation – Synthesizing Missing Data for Handwriting Recognition
In this paper, we demonstrate how a generative model can be used to build a better recognizer through the control of content and style. We are building an online handwriting recognizer from a modest amount of training samples. By training our controllable handwriting synthesizer on the same data, we can synthesize handwriting with previously underrepresented content (e.g., URLs and email addresses) and style (e.g., cursive and slanted). Moreover, we propose a framework to analyze a recognizer that is trained with a mixture of real and synthetic training data. We use the framework to optimize…Apple Machine Learning Research
A Platform for Continuous Construction and Serving of Knowledge At Scale
We introduce Saga, a next-generation knowledge construction and serving platform for powering knowledge-based applications at industrial scale. Saga follows a hybrid batch-incremental design to continuously integrate billions of facts about real-world entities and construct a central knowledge graph that supports multiple production use cases with diverse requirements around data freshness, accuracy, and availability. In this paper, we discuss the unique challenges associated with knowledge graph construction at industrial scale, and review the main components of Saga and how they address…Apple Machine Learning Research