High-definition (HD) maps play an essential role in autonomous driving. However, producing HD map needs huge amount of manual annotations and is thus labor intensive and costly, which limits the widespread use of HD map. To improve the productivity and reduce cost, extensive studies have explored automation of HD map production. Existing studies primarily focus on constructing vectorized map elements from vehicle sensing images and point clouds. However, the follow-up procedure of extracting traffic semantics based on vectorized map elements is lack of study, though it is laborious and costly as well. In this paper, we focus on the automation of inferring traffic light controls for HD map production. To be specific, we aim at associating traffic lights with their controlled roads based on vectorized map data. This problem is not trivial in that: 1) the placement of traffic light contrastive to road varies considerably from scene to scene; 2) even if the placement of traffic lights and roads are similar, the road network layout has great influence on the traffic light controls. To tackle the above challenges, we propose a Heterogeneous Interaction model with Stacked Transformers (HIST) that learns representation from vectorized map elements and encodes contextual information via heterogeneous interactions among different types of map elements. We conduct extensive experiments in major cities of China to validate the efficacy of HIST. Results show HIST achieves accuracy ranging from 96.03% to 98.85% in different cities. We further deploy HIST on the HD map production line at AMAP. By incorporating a rule-based confidence system, the whole system achieves the performance with accuracy > 99.9% and automation rate > 85%, which meets the industrial level quality requirement and saves vast amount of human labor.

Constrained discriminative linear transform (CDLT) optimized with Extended Baum-Welch (EBW) has been presented in the literature as a discriminative speaker adaptation method that outperforms the conventional maximum likelihood algorithm. Defining the controlling parameter of EBW to achieve the best performance of speaker adaptation, however, still remains an open question. This paper presents an empirical study on this issue. Results of our experiment suggest that a log-linear relationship exists between the optimal controlling parameter and the amount of data. This relationship can be used to efficiently define the controlling parameter for each test speaker to improve CDLT performance. We also discuss the possibility of generalizing the log-linear rule to a wider range of learning problems because such knowledge can substantially reduce the computation effort for parameter tuning.

Real-time transcription has been shown to be valuable in facilitating non-native speakers' comprehension in real-time communication. Automated speech recognition (ASR) technology is a critical ingredient for its practical deployment. This paper presents a series of studies investigating how the quality of transcripts generated by an ASR system impacts user comprehension and subjective evaluation. Experiments are first presented comparing performance across three different transcription conditions: no transcript, a perfect transcript, and a transcript with Word Error Rate (WER) =20%. We found 20% WER was the most likely critical point for transcripts to be just acceptable and useful. Then we further examined a lower WER of 10% (a lower bound for today's state-of-the-art systems) employing the same experimental design. The results indicated that at 10% WER comprehension performance was significantly improved compared to the no-transcript condition. Finally, implications for further system development and design are discussed.

We performed an empirical study to understand the relative contributions of real-time transcription to a non-native speaker's comprehension in audio/video meetings. 48 participants were assigned to 2 presentation modes (audio, audio+video) and 3 transcription modes (no transcript, real-time transcripts in the streaming mode, transcripts with all past records) in a 3x2 factorial experimental design. The results suggest that comprehension can be significantly improved for both audio and audio+video conditions when real-time transcription is provided. Also, the participants reported positive subjective responses to the presence of real-time transcription in terms of usefulness, preference, and willingness to use such a feature if provided. No cognitive load issues were reported by the participants in the ability to synthesize across modalities. Implications for system development and design, as well as future work utilizing automation speech recognition to provide the transcripts are discussed.

In this paper, we explore an approach to improved confidence measures based on a novel alignment confusion rate (ACR) which integrates alignment information from two different modeling unit sets in Chinese digits recognition system. Both initial-final (IF) phone set and head-body-tail (HBT) models have proven to obtain good recognition performance for connected digit strings. These two different modeling can produce similar results but with different time-marked word boundaries. The objective of our proposed method is combining posterior probability with alignment confusion rate score provided by word alignment of IF-based results to HBT-based reference results that minimizes word error rate to get an effective confidence measure for utterance verification. The efficiency of the proposed algorithm is demonstrated with various experiments on data collected from car-kit microphone.

The Query-by-Humming (QBH) system allows users to retrieve songs by singing/humming. In this paper we propose a phraselevel piecewise linear scaling algorithm for melody match. Musical phrase boundaries are predicted for the query to split it to phrases. The boundaries of melody fragment corresponding to each phrase are allowed for adjusting in a limited scope. The algorithm employs Dynamic Programming and Recursive Alignment to search for the minimal piecewise matching cost upon Linear Scaling at phrase-level. Our experimental results on 5223 melody database show that the proposed algorithm outperforms traditional algorithms. The proposed algorithm gives significant improvements of 17.0%, 14.7% and 4.8% with respect to Linear Scaling, Dynamic Time Wrapping and Recursive Alignment in top-1 rate, respectively. The results show that the proposed algorithm is more efficient than the previous algorithms.

To achieve a good balance between matching accuracy and computation efficiency is a key challenge for query-by-humming (QBH) system. In this paper, we propose an approach of n-gram based fast match. Our n-gram method uses a robust statistical note transcription as well as error compensation method based on the analysis of frequent transcription errors. The effectiveness of our approach has been evaluated on a relatively large melody database with 5223 melodies. The experimental results show that when the searching space was reduced to only 10% of the whole size, 90% of the target melodies were preserved in the candidates, and 88% of the match accuracy of system was kept. Meanwhile, no obvious additional computation was applied.

This paper presents a robust voice-melody transcription system using a speech recognition framework. While many previous voice-melody transcription systems have utilized non-statistical approaches, statistical recognition technology can potentially achieve more robust results. A cepstrum-based acoustic model is employed to avoid the hard-decisions that have to be made when using explicit voiced-unvoiced segmentation and pitch extraction, and a key-independent 4-gram language model is employed to capture prior probabilities of different melodic sequences. Evaluations are done from the perspective of both note recognition error rate and query-by-humming end-to-end performance. The results are compared with three other voice-melody transcription systems. Experiments have shown that our system is state-of-the-art: it is much more robust than other systems on data containing noise, and close to the best of all the systems on the clean data set.