In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Q: In this section, we consider 3 categories of data augmentation techniques that are effectively applicable to video datasets. To further increase training data size and diversity, we can create new audios via superimposing each original audio with additional noisy audios in time domain. To obtain diverse noisy audios, we use AudioSet, which consists of 632 audio event classes and a collection of over 2 million manually-annotated 10-second sound clips from YouTube videos BIBREF28. We consider applying the frequency and time masking techniques – which are shown to greatly improve the performance of end-to-end ASR models BIBREF23 – to our hybrid systems. Similarly, they can be applied online during each epoch of LF-MMI training, without the need for realignment.

Consider each utterance (i.e. after the audio segmentation in Section SECREF5), and we compute its log mel spectrogram with $\nu $ dimension and $\tau $ time steps:

Frequency masking is applied $m_F$ times, and each time the frequency bands $[f_0$, $f_0+ f)$ are masked, where $f$ is sampled from $[0, F]$ and $f_0$ is sampled from $[0, \nu - f)$.

Time masking is optionally applied $m_T$ times, and each time the time steps $[t_0$, $t_0+ t)$ are masked, where $t$ is sampled from $[0, T]$ and $t_0$ is sampled from $[0, \tau - t)$. Data augmentation ::: Speed and volume perturbation
Both speed and volume perturbation emulate mean shifts in spectrum BIBREF18, BIBREF19. To perform speed perturbation of the training data, we produce three versions of each audio with speed factors $0.9$, $1.0$, and $1.1$. The training data size is thus tripled. For volume perturbation, each audio is scaled with a random variable drawn from a uniform distribution $[0.125, 2]$.

A: What are the best within-language data augmentation methods?
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Q: To further improve the performance of our models, we introduce in-house labeled data that we use to fine-tune BERT. For the gender classification task, we manually label an in-house dataset of 1,100 users with gender tags, including 550 female users, 550 male users. We obtain 162,829 tweets by crawling the 1,100 users' timelines. For the dialect identification task, we randomly sample 20,000 tweets for each class from an in-house dataset gold labeled with the same 15 classes as the shared task.

A: What are the in-house data employed?
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Q: Following the previous works, we perform 10-fold cross validation and report the average results. 

A:
How is the robustness of the model evaluated?
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