Question: What model do they use in their method?
Evidence: Speech input is now a common feature for smart devices. In many cases, the user's query involves entities such as a name from a contact list, a location, or a music title. Recognizing entities is particularly challenging for speech recognition because many entities are infrequent or out of the main vocabulary of the system. One way to improve performance is such cases is through the use of a personal language model (LM) which contains the expected user-specific entities. Because each user can have their own personalized LM, it is vital that the speech decoder be able to efficiently load the model on the fly, so it can be used in decoding, without any noticeable increase in latency.
Many state-of-the-art speech recognition decoders are based on the weighted finite state transducer (WFST) paradigm BIBREF0, BIBREF1. A conventional WFST decoder searches a statically composed $H C L G$ graph, where $H$ is the graph that translates HMM states to CD phones, $C$ translates CD phones to graphemes, $L$ translates graphemes to words and $G$ is graph that represents the language model. Using a statically composed graph has two limitations. First, it is both compute and memory intensive when the vocabulary and LM are large. Second, the static graph approach makes it hard to handle personalized language models BIBREF2. Many common tasks a user may want to perform with a voice assistant such as making phone calls, messaging to a specific contact or playing favorite music require a personalized language model. A dynamic WFST decoder is better suited for such cases. As denoted in Eq (DISPLAY_FORM1), in a dynamic WFST decoder, $HCL$ is composed and optimized offline, while $G$ is composed on the fly with lazy (on-demand) composition, denoted as $\circ $.
To handle dynamic entities, a class LM $G_c$ is normally used as background $G$ and a personalized LM $G_p$ is replaced on-the-fly, before applying lazy composition.
Since the non-terminal states are composed on-the-fly, it means the states of recognition FST will also contain personalized information that cannot be used by other users or service threads.
In previous work, a method was proposed to do a pre-initialized composition for a non-class LM BIBREF3. However, it the dynamic part is still expanded on-the-fly. In this work, we propose two improvements in order to best leverage class language models. First, we use simpler methods for pre-initialization which do not need to pre-generate decoder state statistics. Second, we propose a two-layer pre-initialization mechanism that also avoids performing dynamic expansion on per user basis. In the two-layer pre-initialization method, we make use of a class LM with class tag. We build a personalized FST that contains the members of the class for each user. Using the FST replacement algorithm, we obtain a personalized language transducer BIBREF4. We perform a pre-composition for all FST states whose transitions do not contain class tags. By doing so, the actual on-demand composition is only required for the states in personalized FST. For a multi-threaded service, the pre-composed FST can be shared by all threads, since it does not contain personalized FST states (non-terminals). The personalized part will be shared for all utterances from the same user, which will take full advantage of memory usage.
Unlike the previous pre-initialization approach that is based on calculating the state statistics BIBREF3, our simplified pre-initialization methods do not rely on pre-calculated state frequencies. Instead, we directly expand the graph with breadth-first search or through a data-driven approach where a small numbers of utterances are processed by the decoder offline. We found that both methods are effective, but the data-driven approach outperforms the breadth first search algorithm. Both methods can be combined to achieve the best performance. Through a series of experiments on a speech recognition task for the calling domain, we found that pre-initialization on the public graph speeds up the decoding time by a factor of three. Futhermore, sharing the private graph further reduces decoding time and results in factor of five improvement in efficiency.
In the two-layer pre-initialization method, we make use of a class LM with class tag.
Answer: A class language model (LM) with class tag.