The part of the sentence "adapt means" is not correct and usable in written English.
To use this phrase correctly you could say "Adapting means..." and then provide an example of what adapting could mean. For example, "Adapting means making changes to the environment to meet the needs of an individual."
In order to adapt mean vectors of the SI model, the MAP adaptation process is composed of two major stages.
Since, in addition, it is well known that adapting mixture weights and variances is not helpful for recognition performance, we focus on how to adapt mean vectors only.
But what if adapting means relocating entire towns away from their traditional lands, as is happening in Alaska, when the once permanently frozen ground starts to melt beneath their feet?
Other applications of prediction algorithms for the next journey of users include, for example, a recommender system for bush taxis such as suggested by Gambs et al. [152], using the predicted next location of users to recommend to pedestrians adapted means of transport that are in their neighborhood.
The adapted means μ ^ ( m ^ ) and variance Σ ^ ( σ ^ 2 ) of the state output (state duration) distributions can be expressed, given the linear transformation A o (A d ) and the bias b o (b d ), under the following form: μ ^ = A o μ - b o, Σ ^ = A o Σ A o ⊤, (5) m ^ = A d m - b d, σ ^ 2 = A d σ 2 A d ⊤. (6).
The function ( t, ω ) → f ( t, ω ) is β × Ϝ measurable, where β is the Borel algebra on [ 0, ∞ ) and Ϝ is the σ-algebra on Ω. f is adapted to Ϝ t, where Ϝ t is the σ-algebra generated by the random variables B ( s ) ; s ≤ t and adapted means that f is determined by B ( s ) ; s ≤ t. E [ ∫ s T f ( t, ω ) 2 d t ] < ∞.
Then subsequently followed the needs relating to adapted means of transportation, medical follow-up, home visit from healthcare personnel, and stimulus and motivation provided by a caregiver.
In the traditional MAP adaptation approach, the adapted mean vector of a Gaussian component can be written as: begin{array}rcl@ mathbf{M}_{c}=frac{r}{N_{c} + r}mathbf{m}_{c}+frac{N_{c}}{N_{c} + r}frac{mathbf{F}_{c}}{L} end{array} (4).
Thus, the final adapted mean vector from EPL1-based sparse speaker adaptation is given as follows: {boldsymbol{mu}}_g^{mathrm{SA}hbox mathrm{E}mathrm{P}mathrm{L}1}=signleft {boldsymbol{mu}}_g^{mathrm{ML}}-{boldsymbol{mu}}_g^{mathrm{SI}}right)odot {boldsymbol{varphi}}_g^{mathrm{SA}hbox mathrm{E}mathrm{P}mathrm{L}1}+{boldsymbol{mu}}_g^{mathrm{SI}}.
In Section 6, we analyze our experimental results on adapted mean vectors and speech recognition performance.
As can also be seen in Fig. 4, from the same ML mean vector, the differently adapted mean vectors are obtained because of the shared standard deviations.
