Pouble Vs. SOTA: Multiple Task Models in the Standards of the Real World

Links table

Abstract and 1 introduction

2 approach

2.1 Architecture

2.2 Multimedia instructions

2.3 Learn the curricula with the teacher is effective

3 experiments

4 results

4.1 Evaluation

4.2 Circular via instructions

4.3 Strategies to improve performance

5 relevant work

6 Conclusion, restrictions, ethics clarification, and references

extension

A.1 Voice encrypted training before training

A.2 Parameters hyper

A.3 tasks

4.1 Evaluation

We evaluate the speech models and the common to the end to the end to the end (E2E-SSLM) to take advantage of the FRX problem on 11 unique tasks across multiple databases and groups. We first assess the ability to understand the basic speech of PREXTVERSE through ASR criteria. Then we evaluate the most complex SLU tasks and linguistic speech tasks in Tables 2 and 3, respectively.

4.1.1 Performance on ASR and SLU tasks

First, we evaluate the performance of verbal models on four general standard ASR data sets, namely, Libri-Test-Clean, Libri-Test-Lotter, Voxpopuli and Commonvoice. WER numbers have been reported for each collection of these data collections in Table 2. Pounderryse ASR is used in grade 2 the same form as the Task-FT Mission 3. However, WER increases in both multi-task-task-task-performing tasks with a similar performance across three of the four test sets. The low performance of the multi -task speech model is likely to be a model designated for the task due to giving a lower weight to ASR data groups when building batches during multi -task training. This was done to balance performance in all tasks, because the distribution of data is unbalanced between different tasks.

When it comes to SLU tasks, the frequent question that was asked is whether the comprehensive model can outperform a successive pipeline that copies the words across ASR and then feeds it into a language model. To investigate this, we have experiences on five semantic understanding tasks using the same basic models as Pounderryse. Text Foundation Model on data from the five SLU tasks was set separately, as we found that the performance of Flan-T5 on these standard test sets is very bad. We also report the performance when feeding the local ground texts in LLM, to provide higher results. In 4 of the five tasks, with the exception of the keywords, the models trained from one side excel to the end of the successive pipeline. In particular, the most used tasks such as the category of intent, signs of openings, and speech translation work better than the successive system, which indicates the effectiveness of our trained models using Pounderverse. We also noticed that the Pounderrse models on the KWS mission excel over the successive pipeline by 10 % in accuracy, while it greatly affects the KWE mission. Since the task of searching for keywords requires a period of attention focusing on a specific word of attention, joint modeling helps to improve accuracy by overcoming the spread of the error in a successive pipeline. We have also conducted a study of detection to determine whether the KWE mission is to benefit from deciphering the common ASR copies and the main words. We noticed a improvement in performance, with the gap closing the successive pipeline. The results of this study are more detailed in the 4.3.2 sub -section. When comparing multiple task models with task speech models, there is a slight deterioration in performance, but the difference is not significant. In general, the multiple task model has been trained with Wavlm encoding or the best RQ encryption into successive systems in majority tasks.

4.1.2 Performance for linguistic tasks

Results in Table 3 show clear performance improvements on various linguistic speech processing tasks when using multi -task learning compared to setting the Wavlm model independently for each task. Specifically, the large model that was trained with multi -task learning using the best audio encoded (multi -task) achieve gains on the 4.8 % WAVLM model on recognition, 6.6 % on vocal morale, and 2.5 % on Accent. More modest gains with a multi-task-task-trained battalion model (multi-task-WLM). The adaptive mix of unified representation of all encryption layers helps improve the performance of multiple tasks in improving the performance of the various linguistic tasks. In general, multi -task learning provides noticeable improvements in the generalization of models and effectiveness through a variety of speech tasks compared to the exact mission of the Wavlm Form. The results shed light on the advantages of learning joint representation through related tasks using multi -task learning techniques.

4.1.3 Compared to Sota Models

Table 4 Standards of Church Models Against Modern Models (SOTA) in five various tasks: identification of automatic speech (ASR), speech translation (ST), intention classification (IC), filling of openings (SF), and identifying emotion (er). Through these tasks, PUNDERVERSE explains competitive or superior performance compared to previous specialized models. When comparing our ASR model for the task, which also acts as a preparation for multi -task composition, for ASR whisper, our model achieves a little better performance on average. However, the multi -task (multi -task) task model leads to a whisper in three of the four test sets. When evaluating the translation of speech through three linguistic pairs, the verbal model for the task

It exceeded smoothly on a couple, while the multi -task speech model has achieved competitively compared to the previous average work. Both models did not work well on the English language of the Roman husband. The general performance of the forms models is limited to the translation of speech significantly on the capabilities of the Fant5 basic language model. The translation capabilities can not exceed the quality of translation provided by Flant5 as a basic language model. To evaluate speech in the tasks of understanding spoken language such as the IC Classification (IC) and filling the openings (SF), we have reformulated the speech model specializing in the task by merging all 69 intentions (both invisible) as well as all openings. This allowed us to compare Pearcial Versevers to the previous work on fully intention and opening groups. The speech model against the competitive performance of the previous SOTA (PF-HBT-Large) achieved the filling of the holes, but it was very late in the classification of intentions with an absolute accuracy of 5 % less. However, the Pounderryse outperformed the same Sota (Frozen-Large) model by 10 % when the encryption weights were frozen during the set. For more an analysis of the gap to the latest cases, we have an experiment that allows to adjust the loudspeaker weights while setting it. This has achieved a resolution of 89.5 %, which matches the previous Sota. This indicates that the performance of the intention classification can overcome the SLURP data group when the full transfer process is carried out. The previously trained to the end of the mission of emotional recognition has achieved an 8 % improvement in the average unwanted summons on the previous trainee model (W2V2-L-Robust). On the contrary, the multi -task speech model was 3 % better than the latest condition. However, one of the main differences is that the previous SOTA work trained on the MSP-PodCAST data group 1.7, while we used version 1.11 for training. The test set has remained the same between the two rituals. In general, the battalion model showed competitive performance compared to previous specialized models in some cases when evaluating it through various tasks.