Subword RNNLM Approximations for Out-Of-Vocabulary Keyword Search
Table 2: KWS performance for both morphs and single characters is presented on Arabic and Finnish datasets along with language model size in number of n-grams. The table reports MTWV and Lattice Recall calculated on first-pass decoded lattices for KNV, RNN5, RNNV and its linear interpolation (KNV+RNNV) with equal weights.
['Segmentation', 'LM', 'Arabic MTWV', 'Arabic Lattice Recall', 'Arabic #N-grams', 'Finnish MTWV', 'Finnish Lattice Recall', 'Finnish #N-grams']
In this work, we perform KWS experiments for Arabic and Finnish OOV words from the evaluation set, which are the hardest to predict as they are unseen in the training set. The word-based LMs that are devoid of any phonetic information will fail at predicting them and have a zero MTWV. Hence, we setup a subword-based keyword search on the above datasets. These LMs are built using morph and single-character subwords. Similarly, the RNNLM approximations (RNN5 and RNNV) have a more competitive performance with KNV at character-level than with the morph units. Furthermore, both the interpolated models (KNV+RNN5 and KNV+RNNV) outperform KNV on characters with larger improvements (at least 15.1% on Arabic and 4.2% on Finnish) than on morphs, where the improvments at best can be 7.2% on Arabic and 1.2% on Finnish. On Arabic and Finnish KWS, KNV+RNNV achieves larger improvements over KNV for different subwords on the Arabic KWS (7.2%–23.3%) than on the Finnish KWS (1.2%–7.6%). Improved KNV performance on Finnish might be dependent on the quality of the underlying acoustic models. In particular, the Finnish ASR has a larger and cleaner (Speecon and Speechdat transcripts are verified) dataset than used in the Arabic ASR. Also, Finnish has a simpler phonetic structure than Arabic. Across the different subword units, RNNV mostly performs better than RNN5 on MTWV, except when using characters on the Finnish KWS. The performance differences are larger across Arabic and Finnish when comparing the interpolated models, with KNV+RNNV performing the overall best. This shows the benefits of using the proposed method, which has a different scoring scheme and access to higher-order n-gram contexts than the probability-conversion method. In most cases, KNV+RNNV achieves a better performance than KNV+RNN5 suggesting that RNNV is able to complement KNV better than RNN5. Similar trends were also observed for the Finnish KWS task.